Updated on 2025/07/02

写真a

 
KONDO Yohan
 
Organization
Academic Assembly Institute of Medicine and Dentistry Health Sciences Professor
Faculty of Medicine School of Health Sciences Radiological Technology Professor
Title
Professor
Other name(s)
Yongbum Lee
External link

Degree

  • 博士(工学) ( 2001.3   岐阜大学 )

Research Areas

  • Life Science / Medical systems

Research History

  • Niigata University   Faculty of Medicine School of Health Sciences Radiological Technology   Professor

    2019.4

  • Niigata University   Faculty of Medicine School of Health Sciences Radiological Technology   Associate Professor

    2013.10 - 2019.3

  • Niigata University   Faculty of Medicine School of Health Sciences   Assistant Professor

    2007.4 - 2013.9

  • Niigata University   Graduate School of Health Sciences Health Sciences   Assistant Professor

    2007.4 - 2013.9

  • Niigata University   Faculty of Medicine School of Health Sciences   Research Assistant

    2001.1 - 2007.3

Professional Memberships

  • The Japanese Society of Medical Imaging and Information Sciences

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  • Japanese Society for Medical and Biomedical Engineering

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  • Japanese Society of Radiological Technology

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  • The institute of Electronics, Information and Communication Engineers

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  • The Japanese Society of Medical Imaging Technology

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Committee Memberships

  • 日本医用画像工学会   代議員  

    2022.4   

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  • Radiological Physics and Technology   Associate Editor  

    2022.1 - 2025.2   

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  • 日本医用画像工学会   論文編集委員  

    2018.4 - 2024.7   

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  • 日本放射線技術学会   演題審査委員  

    2015.4 - 2023.3   

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  • 日本放射線技術学会   論文編集委員  

    2015.4 - 2019.3   

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  • 医用画像情報学会   理事  

    2014.6   

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  • 日本放射線技術学会   学術交流委員会委員  

    2013.4 - 2015.3   

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  • 医用画像情報学会   論文編集委員  

    2008.6 - 2014.5   

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  • 日本放射線技術学会   画像分科会委員  

    2007.4 - 2012.3   

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  • 医用画像情報学会   常務理事  

    2005.6 - 2008.5   

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Papers

  • Radiomic Fingerprints: Automated Personal Identification in Mass Disasters Using Shape-Based Features of Thoracic Vertebral Bodies on CT. Reviewed International journal

    Shota Ichikawa, Yohan Kondo, Masashi Okamoto, Tatsuya Kondo, Naoya Takahashi

    Journal of imaging informatics in medicine   2025.6

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    Language:English   Publishing type:Research paper (scientific journal)  

    A fully automated personal identification method is crucial for mass disaster response. This study evaluated an approach using shape-based radiomic features of thoracic vertebral bodies on CT scans. This retrospective study included 66 individuals with both antemortem and postmortem CT scans (2007-2014) and 1018 antemortem cases from the Lung Image Database Consortium image collection. Thoracic vertebral bodies (T1-T12) were segmented, and 14 shape-based radiomic features were extracted. Outlier detection using the Mahalanobis distance excluded vertebral bodies with atypical feature distributions. Personal identification was performed using Euclidean distance-based similarity scores, ranking the ten most similar antemortem cases for each postmortem case. The Mann-Whitney U test compared similarity scores, and the Youden index determined the optimal similarity score threshold for one-to-one verification. Of the 66 individuals, five cases were excluded due to outlier detection. A top-1 match rate of 98.4% (60/61) was achieved for the remaining 61 postmortem cases. Similarity scores for the top-1 rank (median [interquartile range], 0.910 [0.899-0.918]) were significantly higher than those for the top-2 rank (0.834 [0.819-0.853], P < 0.001). The area under the receiver operating characteristic curve was 0.99938, with an optimal similarity score threshold of 0.832, enabling clear differentiation between matches and nonmatches. An automated identification method using shape-based radiomic features of thoracic vertebral bodies on CT scans achieved near-perfect top-1 accuracy, demonstrating its potential for victim identification in mass disasters.

    DOI: 10.1007/s10278-025-01571-x

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  • An Ensemble of Deep Convolutional Neural Networks Using Preoperative Computed Tomography Images for Predicting Postoperative Recurrence of Lung Adenocarcinoma Reviewed

    Yuki SASAKI, Yohan KONDO, Tadashi AOKI, Naoya KOIZUMI, Toshiro OZAKI, Manami UMEZU, Hiroshi SEKI

    Advanced Biomedical Engineering   14   219 - 234   2025.5

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society for Medical and Biological Engineering  

    DOI: 10.14326/abe.14.219

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  • Automated Prediction of Thoracic Vertebral Body Diameters from Computed Tomography Scans Using Deep Learning Reviewed

    Shota Ichikawa, Yohan Kondo, Masashi Okamoto, Tatsuya Kondo, Naoya Takahashi

    Journal of Health Sciences of Niigata University   21 ( 1 )   10 - 20   2025.3

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  • A Noncontact Operating System Using Motion Sensors for Image Display to Avoid Communication Errors in Gastrointestinal Fluoroscopy Reviewed

    Mitsuru SATO, Zen HAYAMA, Chihiro TSUCHIDA, Yohan KONDO, Masashi OKAMOTO, Toshihiro OGURA, Hiromitsu DAISAKI

    The Japanese Journal of Ergonomics   61 ( 1 )   51 - 59   2025.2

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Japan Ergonomics Society  

    DOI: 10.5100/jje.61.51

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  • Pilot Study on Using Large Language Models for Educational Resource Development in Japanese Radiological Technologist Exams Reviewed

    Tatsuya Kondo, Masashi Okamoto, Yohan Kondo

    Medical Science Educator   35 ( 2 )   919 - 927   2025.1

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    Abstract

    In this study, we explored the potential application of large language models (LLMs) to the development of educational resources for medical licensure exams in non-English-speaking contexts, focusing on the Japanese Radiological Technologist National Exam. We categorized multiple-choice questions into image-based, calculation, and textual types. We generated explanatory texts using Copilot, an LLM integrated with Microsoft Bing, and assessed their quality on a 0–4-point scale. LLMs achieved high performance for textual questions, which demonstrated their strong capability to process specialized content. However, we identified challenges in generating accurate formulas and performing calculations for calculation questions, as well as in interpreting complex medical images in image-based questions. To address these issues, we suggest using LLMs with programming functionalities for calculations and using keyword-based prompts for medical image interpretation. The findings highlight the active role of educators in managing LLM-supported learning environments, particularly by validating outputs and providing supplementary guidance to ensure accuracy. Furthermore, the rapid evolution of LLM technology necessitates continuous adaptation of utilization strategies to align with their advancing capabilities. In this study, we underscored the potential of LLMs to enhance educational practices in non-English-speaking regions, while addressing critical challenges to improve their reliability and utility.

    DOI: 10.1007/s40670-024-02251-1

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    Other Link: https://link.springer.com/article/10.1007/s40670-024-02251-1/fulltext.html

  • Development and validation of the surmising model for volumetric breast density using X-ray exposure conditions in digital mammography. Reviewed International journal

    Mika Yamamuro, Yoshiyuki Asai, Takahiro Yamada, Yuichi Kimura, Kazunari Ishii, Yohan Kondo

    Medical & biological engineering & computing   63 ( 1 )   169 - 179   2025.1

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients' age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18-36 mm; standard group, 38-46 mm; and thick group, 48-78 mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (p = 0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients' age, even in the absence of a mammogram image.

    DOI: 10.1007/s11517-024-03186-w

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    Other Link: https://link.springer.com/article/10.1007/s11517-024-03186-w/fulltext.html

  • Effect of frame rate on image quality in cardiology evaluated using an indirect conversion dynamic flat-panel detector. Reviewed

    Akira Hasegawa, Yohan Kondo

    Radiological physics and technology   17 ( 4 )   947 - 954   2024.12

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    To verify the effect of the frame rate on image quality in cardiology, we used an indirect conversion dynamic flat-panel detector (FPD). We quantified the input-output characteristics, and determined the modulation transfer function (MTF) and normalized noise power spectrum (NNPS) of the equipment used in cardiology at 7.5, 10, 15, and 30 frames per second (fps). We also calculated the noise power spectrum for still images and videos at all frame rates and obtained the image lag correction factor r. The input-output characteristics and the MTF agreed even when the frame rate was varied. The NNPS tended to decrease uniformly as a function of frequency at increasing frame rates. The factor r decreased as a function of the frame rate, and its minimum value was 30 fps. Our results suggest that high-frame-rate imaging in cardiology using indirect conversion dynamic FPDs is affected by image lag.

    DOI: 10.1007/s12194-024-00845-3

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  • Deep learning-based correction for time truncation in cerebral computed tomography perfusion. Reviewed

    Shota Ichikawa, Makoto Ozaki, Hideki Itadani, Hiroyuki Sugimori, Yohan Kondo

    Radiological physics and technology   17 ( 3 )   666 - 678   2024.9

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    Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.

    DOI: 10.1007/s12194-024-00818-6

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    Other Link: https://link.springer.com/article/10.1007/s12194-024-00818-6/fulltext.html

  • Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection. Reviewed International journal

    Haruyuki Watanabe, Yuina Ezawa, Eri Matsuyama, Yohan Kondo, Norio Hayashi, Sho Maruyama, Toshihiro Ogura, Masayuki Shimosegawa

    Journal of X-ray science and technology   32 ( 4 )   1151 - 1162   2024.8

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:IOS Press  

    BACKGROUND: Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation. OBJECTIVE: To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation. METHODS: Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods. RESULTS: The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%. CONCLUSIONS: Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.

    DOI: 10.3233/XST-230431

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  • Automated angular measurement for puncture angle using a computer-aided method in ultrasound-guided peripheral insertion. Reviewed International journal

    Haruyuki Watanabe, Hironori Fukuda, Yuina Ezawa, Eri Matsuyama, Yohan Kondo, Norio Hayashi, Toshihiro Ogura, Masayuki Shimosegawa

    Physical and engineering sciences in medicine   47 ( 2 )   679 - 689   2024.6

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    Ultrasound guidance has become the gold standard for obtaining vascular access. Angle information, which indicates the entry angle of the needle into the vein, is required to ensure puncture success. Although various image processing-based methods, such as deep learning, have recently been applied to improve needle visibility, these methods have limitations, in that the puncture angle to the target organ is not measured. We aim to detect the target vessel and puncture needle and to derive the puncture angle by combining deep learning and conventional image processing methods such as the Hough transform. Median cubital vein US images were obtained from 20 healthy volunteers, and images of simulated blood vessels and needles were obtained during the puncture of a simulated blood vessel in four phantoms. The U-Net architecture was used to segment images of blood vessels and needles, and various image processing methods were employed to automatically measure angles. The experimental results indicated that the mean dice coefficients of median cubital veins, simulated blood vessels, and needles were 0.826, 0.931, and 0.773, respectively. The quantitative results of angular measurement showed good agreement between the expert and automatic measurements of the puncture angle with 0.847 correlations. Our findings indicate that the proposed method achieves extremely high segmentation accuracy and automated angular measurements. The proposed method reduces the variability and time required in manual angle measurements and presents the possibility where the operator can concentrate on delicate techniques related to the direction of the needle.

    DOI: 10.1007/s13246-024-01397-x

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  • Contactless Operation System Development for Radiographic Consoles using a Motion Controller to Prevent Unknown Viruses: Proof of Concept Reviewed

    Mitsuru Sato, Chihiro Tsuchida, Zen Hayama, Yohan Kondo, Masashi Okamoto, Toshihiro Ogura, Hiromitsu Daisaki

    Medical Imaging and Information Sciences   41 ( 1 )   15 - 21   2024.3

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    DOI: 10.11318/mii.41.15

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  • Performance evaluation of pocket echo using an ultrasonic accuracy control phantom Reviewed

    Norimitsu Shinohara, Yohan Kondo

    Acta IMEKO   12 ( 4 )   article43   2023.12

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    Publishing type:Research paper (scientific journal)   Publisher:IMEKO International Measurement Confederation  

    In addition to being used in hospitals, ultrasound systems are used in many other medical settings such as disaster relief and home care. In these types of settings, it is important to be able to perform a large number of examinations easily and efficiently. Portable ultrasound systems can be used to meet such needs. The evaluation of ultrasound systems has been driven by the development of accuracy control methods used in breast examinations. This study aimed to evaluate the performance of portable ultrasound systems that have not yet been fully investigated. The performance of two ultrasound systems was evaluated using three measures. For physical evaluation, the change in the mean pixel value of the target and the contrast-to-noise ratio were obtained for each ultrasound system. Statistical analyses were performed to compare these measures between the two systems. For visual evaluation, a receiver operating characteristic analysis was performed. The results of the physical and visual evaluations showed no statistically significant differences between the portable ultrasound systems we evaluated and those that are commonly used in clinical practice.

    DOI: 10.21014/actaimeko.v12i4.1382

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  • Automated Classification of Abdominal Coronal CT Images for Presence/Absence of Traumatic Hematoma using DCNN Reviewed

    Kotaro Miyazawa, Yohan Kondo, Yoshiyuki Noto, Kenichi Sakai, Naoya Takahashi, Ryuta Sasamoto

    Medical Imaging and Information Sciences   40 ( 3 )   56 - 60   2023.9

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    DOI: 10.11318/mii.40.56

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  • Verification of the scalp morphological features of chemotherapy-induced hair loss by image analysis Reviewed

    Kimiko Izumida, Yohan Kondo, Manami Tamura, Yu Koyama, Tomoe Yokono, Momoe Sakagami, Abeywickrama, Hansani Madushika, Mieko Uchiyama

    73 ( 3 )   191 - 198   2023.8

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  • Development of a new body weight estimation method using head CT scout images Reviewed

    Tatsuya Kondo, Manami Umezu, Yohan Kondo, Mitsuru Sato, Tsutomu Kanazawa, Yoshiyuki Noto

    Journal of X-Ray Science and Technology   31 ( 5 )   1 - 13   2023.7

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    BACKGROUND: Imaging examinations are crucial for diagnosing acute ischemic stroke, and knowledge of a patient’s body weight is necessary for safe examination. To perform examinations safely and rapidly, estimating body weight using head computed tomography (CT) scout images can be useful. OBJECTIVE: This study aims to develop a new method for estimating body weight using head CT scout images for contrast-enhanced CT examinations in patients with acute ischemic stroke. METHODS: This study investigates three weight estimation techniques. The first utilizes total pixel values from head CT scout images. The second one employs the Xception model, which was trained using 216 images with leave-one-out cross-validation. The third one is an average of the first two estimates. Our primary focus is the weight estimated from this third new method. RESULTS: The third new method, an average of the first two weight estimation methods, demonstrates moderate accuracy with a 95% confidence interval of ±14.7 kg. The first method, using only total pixel values, has a wider interval of ±20.6 kg, while the second method, a deep learning approach, results in a 95% interval of ±16.3 kg. CONCLUSIONS: The presented new method is a potentially valuable support tool for medical staff, such as doctors and nurses, in estimating weight during emergency examinations for patients with acute conditions such as stroke when obtaining accurate weight measurements is not easily feasible.

    DOI: 10.3233/xst-230087

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  • Quality control system for mammographic breast positioning using deep learning Reviewed

    Haruyuki Watanabe, Saeko Hayashi, Yohan Kondo, Eri Matsuyama, Norio Hayashi, Toshihiro Ogura, Masayuki Shimosegawa

    Scientific Reports   13 ( 1 )   2023.5

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    Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    Abstract

    This study proposes a deep convolutional neural network (DCNN) classification for the quality control and validation of breast positioning criteria in mammography. A total of 1631 mediolateral oblique mammographic views were collected from an open database. We designed two main steps for mammographic verification: automated detection of the positioning part and classification of three scales that determine the positioning quality using DCNNs. After acquiring labeled mammograms with three scales visually evaluated based on guidelines, the first step was automatically detecting the region of interest of the subject part by image processing. The next step was classifying mammographic positioning accuracy into three scales using four representative DCNNs. The experimental results showed that the DCNN model achieved the best positioning classification accuracy of 0.7836 using VGG16 in the inframammary fold and a classification accuracy of 0.7278 using Xception in the nipple profile. Furthermore, using the softmax function, the breast positioning criteria could be evaluated quantitatively by presenting the predicted value, which is the probability of determining positioning accuracy. The proposed method can be quantitatively evaluated without the need for an individual qualitative evaluation and has the potential to improve the quality control and validation of breast positioning criteria in mammography.

    DOI: 10.1038/s41598-023-34380-9

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    Other Link: https://www.nature.com/articles/s41598-023-34380-9

  • Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area. Reviewed

    Shu Onodera, Yohan Kondo, Shoko Ishizawa, Tomoyoshi Kawabata, Hiroki Ishii

    Radiological physics and technology   2023.4

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    This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum efficiency (DQE) by considering clinical conditions to evaluate the image quality. We then measured the segmentation accuracy using a U-net convolutional network to verify the effectiveness of the Cu filter. We obtained images of simulated lung tumors using 10-mm acrylic spheres positioned at the right lung apex and left middle lung of an adult chest phantom. The Dice coefficient was calculated as the similarity between the output and learning images to evaluate the accuracy of tumor area segmentation using U-net. Our results showed that effective DQE was higher in the following order up to the spatial frequency of 2 cycles/mm: 120 kV + no Cu, 120 kV + Cu 0.1 mm, and 120 kV + Cu 0.2 mm. The segmented region was similar to the true region for mass-area extraction in the left middle lobe. The lesion segmentation in the upper right lobe with 120 kV + no Cu and 120 kV + Cu 0.1 mm was less successful. However, adding a Cu filter yielded reproducible images with high Dice coefficients, regardless of the tumor location. We confirmed that adding a Cu filter decreases the X-ray absorption efficiency while improving the signal-to-noise ratio (SNR). Furthermore, artificial intelligence accurately segments low-contrast lesions.

    DOI: 10.1007/s12194-023-00719-0

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  • Deep learning-based classification of adequate sonographic images for self-diagnosing deep vein thrombosis Reviewed

    Yusuke Nakayama, Mitsuru Sato, Masashi Okamoto, Yohan Kondo, Manami Tamura, Yasuko Minagawa, Mieko Uchiyama, Yosuke Horii

    PLOS ONE   18 ( 3 )   e0282747 - e0282747   2023.3

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Public Library of Science (PLoS)  

    Background

    Pulmonary thromboembolism is a serious disease that often occurs in disaster victims evacuated to shelters. Deep vein thrombosis is the most common reason for pulmonary thromboembolism, and early prevention is important. Medical technicians often perform ultrasonography as part of mobile medical screenings of disaster victims but reaching all isolated and scattered shelters is difficult. Therefore, deep vein thrombosis medical screening methods that can be easily performed by anyone are needed. The purpose of this study was to develop a method to automatically identify cross-sectional images suitable for deep vein thrombosis diagnosis so disaster victims can self-assess their risk of deep vein thrombosis.

    Methods

    Ultrasonographic images of the popliteal vein were acquired in 20 subjects using stationary and portable ultrasound diagnostic equipment. Images were obtained by frame split from video. Images were classified as “Satisfactory,” “Moderately satisfactory,” and “Unsatisfactory” according to the level of popliteal vein visualization. Fine-tuning and classification were performed using ResNet101, a deep learning model.

    Results

    Acquiring images with portable ultrasound diagnostic equipment resulted in a classification accuracy of 0.76 and an area under the receiver operating characteristic curve of 0.89. Acquiring images with stationary ultrasound diagnostic equipment resulted in a classification accuracy of 0.73 and an area under the receiver operating characteristic curve of 0.88.

    Conclusion

    A method for automatically identifying appropriate diagnostic cross-sectional ultrasonographic images of the popliteal vein was developed. This elemental technology is sufficiently accurate to automatically self-assess the risk of deep vein thrombosis by disaster victims.

    DOI: 10.1371/journal.pone.0282747

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  • Severe Acute Respiratory Syndrome Coronavirus 2(SARS-Cov-2)の感染対策のためのポータブルX線撮影コンソールの非接触操作システムの開発

    佐藤 充, 土田 千裕, 巴山 禅, 近藤 世範, 岡本 昌士

    日本放射線技術学会総会学術大会予稿集   79回   295 - 295   2023.3

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  • 複合現実装置を用いたモニタ上に表示されたCT colonography画像を識別するアプリケーションの開発;初期検討 Reviewed

    佐藤 充, 小倉敏裕, 近藤世範, 成田瑞生, 梅室愛華, 中山智美, 中澤綾乃

    日本消化管CT技術学会誌   21   25 - 33   2023.3

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  • CT colonographyの体位を識別する深層学習モデルの開発 Reviewed

    佐藤 充, 小倉敏裕, 近藤世範, 成田瑞生, 梅室愛華, 中山智美, 中澤綾乃

    日本消化管CT技術学会   21   3 - 10   2023.3

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  • 大腸Computed tomographyの画像種類を識別する深層学習モデルの開発 Reviewed

    佐藤 充, 小倉敏裕, 近藤世範, 成田瑞生, 梅室愛華, 中山智美, 中澤綾乃

    日本消化管CT技術学会   21   11 - 16   2023.3

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  • Ensemble学習によるVirtual endoscopy及びMulti-planar reconstructionのFusion画像の体位推定手法の開発 Reviewed

    佐藤 充, 小倉敏裕, 近藤世範, 成田瑞生, 梅室愛華, 中山智美, 中澤綾乃

    日本消化管CT技術学会   21   17 - 24   2023.3

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  • Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT. Reviewed International journal

    Yuki Sasaki, Yohan Kondo, Tadashi Aoki, Naoya Koizumi, Toshiro Ozaki, Hiroshi Seki

    International journal of computer assisted radiology and surgery   17 ( 9 )   1651 - 1661   2022.9

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN). METHODS: This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence. RESULTS: The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86. CONCLUSION: We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.

    DOI: 10.1007/s11548-022-02694-0

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  • Development of individual identification method using thoracic vertebral features as biometric fingerprints Reviewed

    Mitsuru Sato, Yohan Kondo, Masashi Okamoto, Naoya Takahashi

    Scientific Reports   12 ( 1 )   16274-1 - 16274-11   2022.9

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    Abstract

    Identification of individuals is performed when a corpse is found after a natural disaster, incident, or accident. DNA and dental records are frequently used as biometric fingerprints; however, identification may be difficult in some cases due to decomposition or damage to the corpse. The present study aimed to develop an individual identification method using thoracic vertebral features as a biological fingerprint. In this method, the shortest diameter in height, width, and depth of the thoracic vertebrae in the postmortem image and a control antemortem were recorded and a database was compiled using this information. The Euclidean distance or the modified Hausdorff distance was calculated as the distance between two points on the three-dimensional feature space of these measurement data. The thoracic vertebrae T1-12 were measured and the pair with the smallest distance was considered to be from the same person. The accuracy of this method for identifying individuals was evaluated by matching images of 82 cases from a total of 702 antemortem images and showed a hit ratio of 100%. Therefore, this method may be used to identify individuals with high accuracy.

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  • Development of a computer-aided quality assurance support system for identifying hand X-ray image direction using deep convolutional neural network. Reviewed

    Mitsuru Sato, Yohan Kondo, Masashi Okamoto

    Radiological physics and technology   2022.8

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    The convenience of imaging has improved with digitization; however, there has been no progress in the methods used to prevent human error. Therefore, radiographic incidents and accidents are not prevented. In Japan, image interpretation is conducted for incident prevention; nevertheless, in some cases, incidents are overlooked. Thus, assistance from a computer-aided quality assurance support system is important. This study developed a method to identify hand image direction, which is an elementary technology of a computer-aided quality assurance support system. In total, 14,236 hand X-ray images were used to classify hand directions (upward, downward, rightward, and leftward) commonly evaluated in clinical settings. The accuracy of the conventional classification method using original images, classification method with histogram equation images, and a novel classification method using binarization images for background removal via U-Net segmentation was evaluated. The following classification accuracy rates were achieved: 89.20% if the original image was input, 99.10% if the histogram equation image was input, and 99.70% if binarization images for background removal via U-Net segmentation was input. Our computer-aided quality assurance support system can be used to identify hand direction with high accuracy.

    DOI: 10.1007/s12194-022-00675-1

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  • Robustness of a U-net model for different image processing types in segmentation of the mammary gland region Reviewed

    Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Hiroto Kimura, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Seiun Nin, Kazunari Ishii, Yongbum Lee

    16th International Workshop on Breast Imaging (IWBI2022)   2022.7

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    DOI: 10.1117/12.2624139

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  • Utility of U-Net for the objective segmentation of the fibroglandular tissue region on clinical digital mammograms. Reviewed International journal

    Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Hiorto Kimura, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Seiun Nin, Kazunari Ishii, Yohan Kondo

    Biomedical physics & engineering express   8 ( 4 )   045016   2022.6

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    Abstract

    This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the ‘dense region’); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the ‘diffuse region’). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland–Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland–Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were −0.0299 and −0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts.

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  • A deep convolutional neural network to predict the curve progression of adolescent idiopathic scoliosis: a pilot study. Reviewed International journal

    Yasuhito Yahara, Manami Tamura, Shoji Seki, Yohan Kondo, Hiroto Makino, Kenta Watanabe, Katsuhiko Kamei, Hayato Futakawa, Yoshiharu Kawaguchi

    BMC musculoskeletal disorders   23 ( 1 )   610 - 610   2022.6

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    Abstract

    Background

    Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that predominantly occurs in girls. While skeletal growth and maturation influence the development of AIS, accurate prediction of curve progression remains difficult because the prognosis for deformity differs among individuals. The purpose of this study is to develop a new diagnostic platform using a deep convolutional neural network (DCNN) that can predict the risk of scoliosis progression in patients with AIS.

    Methods

    Fifty-eight patients with AIS (49 females and 9 males; mean age: 12.5 ± 1.4 years) and a Cobb angle between 10 and 25 degrees (mean angle: 18.7 ± 4.5) were divided into two groups: those whose Cobb angle increased by more than 10 degrees within two years (progression group, 28 patients) and those whose Cobb angle changed by less than 5 degrees (non-progression group, 30 patients). The X-ray images of three regions of interest (ROIs) (lung [ROI1], abdomen [ROI2], and total spine [ROI3]), were used as the source data for learning and prediction. Five spine surgeons also predicted the progression of scoliosis by reading the X-rays in a blinded manner.

    Results

    The prediction performance of the DCNN for AIS curve progression showed an accuracy of 69% and an area under the receiver-operating characteristic curve of 0.70 using ROI3 images, whereas the diagnostic performance of the spine surgeons showed inferior at 47%. Transfer learning with a pretrained DCNN contributed to improved prediction accuracy.

    Conclusion

    Our developed method to predict the risk of scoliosis progression in AIS by using a DCNN could be a valuable tool in decision-making for therapeutic interventions for AIS.

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  • Dose reduction potential in dual-energy subtraction chest radiography based on the relationship between spatial-resolution property and segmentation accuracy of the tumor area Reviewed

    Shu Onodera, Yongbum Lee, Tomoyoshi Kawabata

    Acta IMEKO   11 ( 2 )   Article 28 - 1   2022.6

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    &lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;We investigated the relationship between the spatial-resolution property of soft tissue images and the lesion detection ability using U-net. We aimed to explore the possibility of dose reduction during energy subtraction chest radiography. The correlation between the spatial-resolution property of each dose image and the segmentation accuracy of the tumor area in the four regions where the tumor was placed was evaluated using linear regression analysis. The spatial-resolution property was determined by task-based evaluation, and the task-based modulation transfer function (TTF) was computed as its index. TTFs of the reference dose image and the 75 % dose image showed almost the same frequency characteristics regardless of the location of the tumor, and the Dice coefficient also high. When the tumor was located in the right supraclavicular region and under 50 % dose, the frequency characteristics were significantly reduced, and the Dice coefficient was also low. Our results showed a close relationship between the spatial-resolution property and the segmentation accuracy of tumor area using deep learning in dual-energy subtraction chest radiography. In conclusion, a dose reduction of approximately 25 % compared to the conventional method can be achieved. The limitations are the shape of the simulated mass and the use of chest phantom.&lt;/span&gt;&lt;/p&gt;

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  • U-Net-based image segmentation of the whole heart and four chambers on pediatric X-ray computed tomography. Reviewed

    Akifumi Yoshida, Yohan Kondo, Norihiko Yoshimura, Tatsuya Kuramoto, Akira Hasegawa, Tsutomu Kanazawa

    Radiological physics and technology   15 ( 2 )   156 - 169   2022.6

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    This study aimed to determine whether a U-Net-based segmentation method could be used to automatically extract regions of the whole heart and atrioventricular regions from pediatric cardiac computed tomography images with high accuracy. Pediatric cardiac contrast computed tomography images with no abnormalities (n = 20; patient age, 0-13 years; mean 5 years) were used for segmentation of the whole heart and each atrioventricular region using U-Net. Segmentation accuracy was evaluated using the Dice similarity coefficient. The mean Dice similarity coefficient for the whole-heart segmentation was high at 0.95. There were no significant differences between age categories. The median Dice similarity coefficients for segmentation of the atria and ventricles were good (> 0.86). There were significant differences between age categories at some sites. Differences in the Dice similarity coefficient may have occurred because the target diseases and examination procedures differed according to subject age. There was no clear tendency for similar values between subjects of school age, close to adulthood, and newborns; good agreement was obtained in all age categories. These results suggest that U-Net-based segmentation may be useful for automatic extraction of the whole heart and atrioventricular regions from pediatric computed tomography images.

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  • Development of a contactless operation system for radiographic consoles using an eye tracker for severe acute respiratory syndrome coronavirus 2 infection control: a feasibility study Reviewed

    Mitsuru Sato, Mizuki Narita, Naoya Takahashi, Yohan Kondo, Masashi Okamoto, Toshihiro Ogura

    Acta IMEKO   11 ( 2 )   Article 38 - 1   2022.6

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    &lt;p class="Abstract"&gt;Sterilization of medical equipment in isolation wards is essential to prevent the transmission of severe acute respiratory syndrome coronavirus 2 (SARS–CoV-2) infection. Particularly, the radiographic console of portable X-ray machines requires frequent disinfection because it is regularly moved; this requires considerable infection control effort as the number of patients with coronavirus disease 2019 (COVID-19) increases. To evaluate the application of a system facilitating noncontact operation of radiographic consoles for patients with COVID-19 to reduce the need for frequent disinfection. We developed a noncontact operation system for radiographic consoles that used a common eye tracker. We compared calibration errors between with and without face shield conditions. Moreover, the use of console operation among 41 participants was investigated. The calibration error of the eye tracker between with and without face shield conditions did not significantly differ. All (&lt;em&gt;n&lt;/em&gt; = 41) observers completed the console operation. Pearson’s correlation coefficient analysis showed a strong correlation (&lt;em&gt;r&lt;/em&gt; = 0.92, &lt;em&gt;P&lt;/em&gt; &amp;lt; 0.001) between the average operation time and the average number of misoperations. Our system that used an eye tracker can be applied even if the operator uses a face shield. Thus, its application is important in preventing the transmission of infection.&lt;/p&gt;

    DOI: 10.21014/acta_imeko.v11i2.1272

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  • Development of an automatic multiplanar reconstruction processing method for head computed tomography. Reviewed International journal

    Mitsuru Sato, Yohan Kondo, Noriyuki Takahashi, Tomomi Ohmura, Naoya Takahashi

    Journal of X-ray science and technology   30 ( 4 )   777 - 788   2022.5

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    BACKGROUND: Head computed tomography (CT) is a commonly used imaging modality in radiology facilities. Since multiplanar reconstruction (MPR) processing can produce different results depending on the medical staff in charge, there is a possibility that the antemortem and postmortem images of the same person could be assessed and identified differently. OBJECTIVE: To propose and test a new automatic MPR method in order to address and overcome this limitation. METHODS: Head CT images of 108 cases are used. We employ the standardized transformation of statistical parametric mapping 8. The affine transformation parameters are obtained by standardizing the captured CT images. Automatic MPR processing is performed by using this parameter. The sphenoidal sinus of the orbitomeatal cross section of the automatic MPR processing of this study and the conventional manual MPR processing are cropped with a matrix size of 128×128, and the value of zero mean normalized correlation coefficient is calculated. RESULTS: The computed zero mean normalized cross-correlation coefficient (Rzncc) of≥0.9, 0.8≤Rzncc <  0.9 and 0.7≤Rzncc <  0.8 are achieved in 105 cases (97.2%), 2 cases (1.9%), and 1 case (0.9%), respectively. The average Rzncc was 0.96±0.03. CONCLUSION: Using the proposed new method in this study, MPR processing with guaranteed accuracy is efficiently achieved.

    DOI: 10.3233/XST-221142

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  • 深層学習を用いた頭部単純X線画像における撮影精度の評価

    光武 秀悦, 渡部 晴之, 坂口 彩, 内山 喜代志, 李 鎔範, 林 則夫, 下瀬川 正幸, 小倉 敏裕

    日本放射線技術学会雑誌   78 ( 1 )   23 - 32   2022.1

  • [Evaluation of Radiograph Accuracy in Skull X-ray Images Using Deep Learning]. Reviewed

    Hideyoshi Mitsutake, Haruyuki Watanabe, Aya Sakaguchi, Kiyoshi Uchiyama, Yongbum Lee, Norio Hayashi, Masayuki Shimosegawa, Toshihiro Ogura

    Nihon Hoshasen Gijutsu Gakkai zasshi   78 ( 1 )   23 - 32   2022

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    PURPOSE: Accurate positioning is essential for radiography, and it is especially important to maintain image reproducibility in follow-up observations. The decision on re-taking radiographs is entrusting to the individual radiological technologist. The evaluation is a visual and qualitative evaluation and there are individual variations in the acceptance criteria. In this study, we propose a method of image evaluation using a deep convolutional neural network (DCNN) for skull X-ray images. METHOD: The radiographs were obtained from 5 skull phantoms and were classified by simple network and VGG16. The discrimination ability of DCNN was verified by recognizing the X-ray projection angle and the retake of the radiograph. DCNN architectures were used with the different input image sizes and were evaluated by 5-fold cross-validation and leave-one-out cross-validation. RESULT: Using the 5-fold cross-validation, the classification accuracy was 99.75% for the simple network and 80.00% for the VGG16 in small input image sizes, and when the input image size was general image size, simple network and VGG16 showed 79.58% and 80.00%, respectively. CONCLUSION: The experimental results showed that the combination between the small input image size, and the shallow DCNN architecture was suitable for the four-category classification in X-ray projection angles. The classification accuracy was up to 99.75%. The proposed method has the potential to automatically recognize the slight projection angles and the re-taking images to the acceptance criteria. It is considered that our proposed method can contribute to feedback for re-taking images and to reduce radiation dose due to individual subjectivity.

    DOI: 10.6009/jjrt.780104

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  • Relationship between task-based modulation transfer function and evaluation index of tumor area in dual energy subtraction chest radiography Reviewed

    Shu Onodera, Yongbum Lee, Tomoyoshi Kawabata

    Measurement: Sensors   18   100089 - 100089   2021.12

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    DOI: 10.1016/j.measen.2021.100089

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  • Automated heart segmentation using U-Net in pediatric cardiac CT Reviewed

    Akifumi Yoshida, Yongbum Lee, Norihiko Yoshimura, Tatsuya Kuramoto, Akira Hasegawa, Tsutomu Kanazawa

    Measurement: Sensors   18   100127 - 100127   2021.12

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    DOI: 10.1016/j.measen.2021.100127

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  • Pythonを使った深層学習による画像の分類・推定・領域分割・異常検知・画像生成(初~中級者向けプログラミングコース)

    原 武史, 李 鎔範, 篠原 範充, 二上 菜津実, 飯島 康太郎

    日本放射線技術学会総会学術大会予稿集   77回   89 - 89   2021.3

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  • How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography

    Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Yoshiaki Ozaki, Seiun Nin, Kazunari Ishii, Yongbum Lee

    Medical Imaging 2021: Computer-Aided Diagnosis   2021.2

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  • The effect of breast density on the missed lesion rate in screening digital mammography determined using an adjustable-density breast phantom tailored to Japanese women Reviewed International journal

    Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Yoshiaki Ozaki, Kazunari Ishii, Yongbum Lee

    PLOS ONE   16 ( 1 )   e0245060 - e0245060   2021.1

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    <sec id="sec001"><title>Objective</title>Despite the high risk of missing lesions in mammography, the missed lesion rate is yet to be clinically established. Further, no breast phantoms with adjustable breast density currently exist. We developed a novel, adjustable-density breast phantom with a composition identical to that of actual breasts, and determined the quantitative relationship between breast density and the missed lesion rate in mammography.

    </sec><sec id="sec002"><title>Methods</title>An original breast phantom consisting of adipose- and fibroglandular-equivalent materials was developed, and a receiver operating characteristic (ROC) study was performed. Breast density, which is the fraction by weight of fibroglandular to total tissue, was adjusted to 25%, 50%, and 75% by arbitrarily mixing the two materials. Microcalcification, mass lesions, and spiculated lesions, each with unique characteristics, were inserted into the phantom. For the above-mentioned fibroglandular densities, 50 positive and 50 negative images for each lesion type were used as case samples for the ROC study. Five certified radiological technologists participated in lesion detection.

    </sec><sec id="sec003"><title>Results</title>The mass-lesion detection rate, according to the area under the curve, decreased by 18.0% (<italic>p</italic>= 0.0001, 95% Confidence intervals [CI] = 0.1258 to 0.1822) and 37.8% (<italic>p</italic>= 0.0003, 95% CI = 0.2453 to 0.4031) for breast densities of 50% and 75%, respectively, compared to that for a 25% breast density. A similar tendency was observed with microcalcification; however, spiculated lesions did not follow this tendency.

    </sec><sec id="sec004"><title>Conclusions</title>We quantified the missed lesion rate in different densities of breast tissue using a novel breast phantom, which is imperative for advancing individualized screening mammography.

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  • Evaluation of dose reduction potential in scatter-corrected bedside chest radiography using U-net Reviewed

    Shu Onodera, Yongbum Lee, Yoshitaka Tanaka

    Radiological Physics and Technology   13 ( 4 )   336 - 347   2020.12

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    Bedside radiography has increasingly attracted attention because it allows for immediate image diagnosis after X-ray imaging. Currently, wireless flat-panel detectors (FPDs) are used for digital radiography. However, adjustment of the X-ray tube and FPD alignment are extremely difficult tasks. Furthermore, to prevent a poor image quality caused by scattered X-rays, scatter removal grids are commonly used. In this study, we proposed a scatter-correction processing method to reduce the radiation dose when compared with that required by the X-ray grid for the segmentation of a mass region using deep learning during bedside chest radiography. A chest phantom and an acrylic cylinder simulating the mass were utilized to verify the image quality of the scatter-corrected chest X-rays with a low radiation dose. In addition, we used the peak signal-to-noise ratio and structural similarity to quantitatively assess the quality of the low radiation dose images compared with normal grid images. Furthermore, U-net was used to segment the mass region during the scatter-corrected chest X-ray with a low radiation dose. Our results showed that when scatter correction is used, an image with a quality equivalent to that obtained by grid radiography is produced, even when the imaging dose is reduced by approximately 20%. In addition, image contrast was improved using scatter radiation correction as opposed to using scatter removal grids. Our results can be utilized to further develop bedside chest radiography systems with reduced radiation doses.

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  • Structure optimization of deep convolutional neural network for automatic classification of calcifications and stents in coronary computed tomography angiography Reviewed

    20 ( 2 )   9 - 15   2020.11

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    Other Link: http://id.ndl.go.jp/bib/031224466

  • Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers’ performance Reviewed

    Yuki Shinohara, Noriyuki Takahashi, Yongbum Lee, Tomomi Ohmura, Atsushi Umetsu, Fumiko Kinoshita, Keita Kuya, Ayumi Kato, Toshibumi Kinoshita

    Japanese Journal of Radiology   38 ( 9 )   870 - 877   2020.9

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  • Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts

    Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Kenta Sakaguchi, Tatsuo Konishi, Koji Yamada, Yoshiaki Ozaki, Kazunari Ishii, Yougbum Lee

    15th International Workshop on Breast Imaging (IWBI2020)   11513   2020.5

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  • Effectiveness of high-luminance display monitors in digital mammography Reviewed

    Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Koji Yamada, Yoshiaki Ozaki, Kazunari Ishii, Yongbum Lee

    15th International Workshop on Breast Imaging (IWBI2020)   11513   2020.5

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  • Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke Reviewed

    Yuki Shinohara, Noriyuki Takahashi, Yongbum Lee, Tomomi Ohmura, Toshibumi Kinoshita

    Japanese Journal of Radiology   38 ( 2 )   2019.10

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    DOI: 10.1007/s11604-019-00894-4

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  • 深層学習による領域抽出技術を用いた散乱線補正処理胸部X線撮影における線量低減可否の検討

    小野寺 崇, 李 鎔範, 田中 良隆

    日本放射線技術学会雑誌   75 ( 9 )   1021 - 1021   2019.9

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  • Removing Unsharpness of Coronary Angiography Moving Images Using Deep Learning Reviewed

    HASEGAWA Akira, NOGUCHI Eika, LEE Yongbum

    Medical Imaging and Information Sciences   36 ( 2 )   98 - 101   2019.6

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    Unsharpnesses are likely to occur with a high heart rate in angiography. In this study, U-Net was used to remove unsharpness for the purpose of improving the image quality of x-ray movies in the cardiovascular imaging. Dynamic x-ray images including unsharpness were taken with the moving speed of the metronome at 100, 200 beats/minute (bpm). Standard deviation(SD)and modulation transfer function(MTF)were measured and used to evaluate the effect of artifact removal. As a result, mean SDs of original images and processed images by U-Net were 4.34 and 0.54, respectively. Similarly, mean cut-off frequencies of MTF of original images and processed images by U-Net were 0.52 mm<sup>−1 </sup>and 4.6 mm<sup>−1</sup>, respectively. Since SD was greatly reduced and MTF was greatly improved, U-Net would improve the image quality of improvement cardiovascular dynamic x-ray images.

    DOI: 10.11318/mii.36.98

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  • Computerized Classification of Right or Left and Directions of Arms in Forearm X-ray Images Using Deep Learning Reviewed

    YAMADA Tomona, LEE Yongbum, HASEGAWA Akira

    Medical Imaging and Information Sciences   36 ( 2 )   83 - 87   2019.6

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    The purpose of this paper is to develop a computerized classification method for right or left and directions of arms in forearm X-ray images using a deep convolutional neural network(DCNN). 648 radiographs were obtained by using X-ray lower arm phantoms. These images were downsized to 213×256 pixels and used as training and test images in the DCNN. AlexNet and GoogLeNet were used as the DCNN. All radiographs were classified to eight categories by the DCNN. Classification accuracies were obtained by nine-fold cross validation tests. The accuracies using AlexNet and GoogLeNet were 79.3% and 92.6%, respectively. GoogLeNet would be useful to classify forearm radiographs automatically. The proposed method may contribute to quality assurance for medical images.

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  • ウェーブレット係数を用いたCNN によるCT 画像の肺がんの組織型分類 Reviewed

    松山江里, 李鎔範, 高橋規之, 蔡篤儀

    医用画像情報学会雑誌   36 ( 2 )   64 - 71   2019.6

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  • Computerized identification of early ischemic changes in acute stroke in noncontrast CT using deep learning

    Noriyuki Takahashi, Yuki Shinohara, Toshibumi Kinoshita, Tomomi Ohmura, Keisuke Matsubara, Yongbum Lee, Hideto Toyoshima

    Proc of SPIE Medical Imaging   10950   109503A:1 - 109503A:6   2019.3

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  • 深層学習による単純CTにおける急性期脳梗塞の自動検出 Reviewed

    高橋規之, 木下俊文, 大村知巳, 松原佳亮, 李鎔範, 豊嶋英仁

    MEDICAL IMAGING TECHNOLOGY   36 ( 5 )   217 - 220   2018.11

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  • Development of Monitoring Method of Respiratory Waveform in Thoracicoabdominal Part Using Web Camera Reviewed

    Lee Yongbum, Hayakawa Takahide, Sasamoto Ryuta

    Japanese Journal of Radiological Technology   74 ( 11 )   1286 - 1292   2018.11

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    Countermeasures against respiratory movement are important for tumors of thorax and abdomen in stereotactic body radiation therapy. In the present paper, a web-camera-based-respiratory monitoring method without contact with patient’s body was proposed for respiratory study. Thoracic and abdominal motion images were taken by a web camera, and were analyzed using simple image-processing techniques for obtaining respiratory waveforms. Four motion images with different respiration rate were obtained from resusci anne simulator. Respiration waveforms were estimated from the moving images by the proposed method, and were compared with respiration waveforms obtained by the conventional respiratory monitoring device. That was found to have a strong correlation. In addition, the two waveforms were similar in Bland–Altman method comparison. The proposed method can provide non-contact, non-invasive, simple, and realistic respiratory monitoring system for radiotherapy.

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  • [Automated Classification of Calcification and Stent on Computed Tomography Coronary Angiography Using Deep Learning]. Reviewed

    Akira Hasegawa, Yongbum Lee, Yu Takeuchi, Katsuhiro Ichikawa

    Nihon Hoshasen Gijutsu Gakkai zasshi   74 ( 10 )   1138 - 1143   2018.10

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    In computed tomography coronary angiography (CTCA), calcification and stent make it difficult to evaluate intravascular lumen. This is a cause of low positive-predictive value of coronary stenosis. Therefore, it is expected to develop a computer-aided diagnosis (CAD) system that can automatically detect stenosis in coronary arteries. The purpose of this study is to automatically recognize calcifications or stents in coronary arteries and classify them from the normal coronary artery in CTCA. We used 4960 coronary-cross-sectional images, which consisted of 1113 images with calcification, 1353 images with a stent, and 2494 normal artery images. These images were automatically classified using the deep convolutional neural network (LeNet, AlexNet, and GoogLeNet). The classification accuracy of LeNet, AlexNet, and GoogLeNet were 58.4%, 75.9%, and 81.3%, respectively. The proposed method would be a fundamental technique of CAD in CTCA.

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  • Temporal averaging method for higher-quality cerebrovascular 4D-CTA Reviewed

    Tomomi Ohmura, Yongbum Lee, Noriyuki Takahashi, Hideto Toyoshima, Yuichiro Sato, Takato Ishida

    Journal of Medical Imaging and Health Informatics   8 ( 5 )   1064 - 1068   2018.6

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  • テクスチャ特徴を用いた超音波画像における乳児股関節の状態分類 Reviewed

    李鎔範, 長谷川晃, 皆川靖子, 弦巻正樹, 伊賀敏朗

    電子情報通信学会論文誌D   J101-D ( 1 )   36 - 39   2018.1

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  • Novel Perfusion Evaluation Method Using Phase-ratio Image Map in Head 4D-CT Reviewed

    73 ( 11 )   1125 - 1131   2017.11

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  • Automated classification of infant hip type on ultrasonography using deep learning : preliminary study Reviewed

    LEE Yongbum, OHSAWA Yoshiaki, HASEGAWA Akira, MINAGAWA Yasuko, TSURUMAKI Masaki, IGA Toshiro

    Medical Imaging and Information Sciences   34 ( 2 )   92 - 95   2017.6

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    The purpose of this study is to investigate an effectiveness of a method for automatic classification of infant hip types on ultrasonography. A convolutional neural network(CNN)was adopted for the automated classification of hip types corresponding to the Graf method that was defacto standard method for ultrasonographic assessment of infant hip dysplasia. In the CNN, AlexNet was employed as neural network model. We collected 49 ultrasound images that were classified based on the Graf method by an ultrasonographer. Data augmentation by rotating, mirroring, adjusting contrast, etc., generated additional 246,960 images from the original 49 ones. The augmented images were used as training data of the CNN. The accuracy by 10-fold cross validation was 73%. The CNN would be potentially effective for automatic classification of infant hip types.

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  • Effects of Communication Robot on Distress Reduction in Mammography Reviewed International journal

    Yongbum Lee, Mieko Uchiyama, Akira Hasegawa, Rika Saitoh

    Journal of Biomedical Science and Engineering   10 ( 3 )   107 - 119   2017.3

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  • 4D-CTにおける脳虚血領域の自動検出 Reviewed

    李 鎔範, 飯島 祐希, 大村 知巳, 長谷川 晃, 高橋 規之

    Medical Imaging Technology   36 ( 1 )   32 - 35   2017.1

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  • Comparison of Wavelet-Coefficient Mapping Methods Used for Medical Image Enhancement Reviewed

    MATSUYAMA Eri, LEE Yongbum, TAKAHASHI Noriyuki, TSAI Du-Yih

    Medical Imaging and Information Sciences   33 ( 3 )   63 - 68   2016.10

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    <p>The purpose of this study is to investigate and compare the effectiveness of two previously proposed waveletbased medical image enhancement methods,<i> i.e</i>., exponential-type (E-type) and sigmoid-type (S-type) mapping methods. A total of 30 chest radiographs, CT images and mammograms were evaluated visually by using Scheffe's method for paired comparison. As a visual evaluation, images obtained with and without enhancement processing, or images processed with the two enhancement methods were displayed side by side on a light emitting diode monitor. Two different images, <i>i.e</i>.,original image vs. image processed with E-type mapping method, original image vs. image processed with S-type mapping method, and image processed with E-type mapping method vs. that with S-type mapping method, were then evaluated on a discrete rating scale. The experimental results showed that the images processed with S-type mapping method are significantly better than the original images obtained with the three different modalities in both the visual and quantitative evaluations. In addition, the images processed with E-type mapping method are significantly better than the original images of chest radiographs in both the evaluations. The results demonstrate that the previously proposed image enhancement methods are effective and feasible.</p>

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  • Automated Selection of Sectional Images including Basal Ganglia from Brain CT Images Reviewed

    LEE Yongbum, TAKAHASHI Noriyuki, HASEGAWA Akira

    Medical Imaging and Information Sciences   33 ( 1 )   16 - 21   2016.3

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    The purpose of this study is to develop an automated selection method for cross‐sectional images with basal ganglia from brain CT scan. The proposed method includes binarization, morphological operation and labeling techniques. Area was calculated from the labeled region inside the cranial bones. The cross‐sectional images with basal ganglia were automatically selected using the calculated area. The method was applied to 22 cases each with 20 slices. As a result, the sensitivity and the specificity were 0.95 and 0.98 respectively. The proposed method would be a fundamental technique for the development of computer‐aided diagnosis(CAD)system.

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  • Z-score-based semi-quantitative analysis of the volume of the temporal horn of the lateral ventricle on brain CT images Reviewed

    Noriyuki Takahashi, Toshibumi Kinoshita, Tomomi Ohmura, Yongbum Lee, Eri Matsuyama, Hideto Toyoshima, Du-Yih Tsai

    Radiological Physics and Technology   9 ( 1 )   69 - 76   2016.1

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  • Concept and practice of genetic algorithm template matching and higher order local autocorrelation schemes in automated detection of lung nodules

    Lee, Y., Hara, T., Tsai, D., Fujita, H.

    Lung Imaging and Computer Aided Diagnosis   2016

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  • Examination of Visual Effect in Low-dose Cerebral CT Perfusion Phantom Image Using Iterative Reconstruction Reviewed

    Ohmura Tomomi, Lee Yongbum, Takahashi Noriyuki, Sato Yuichiro, Ishida Takato, Toyoshima Hideto

    Jpn. J. Radiol. Technol.   71 ( 11 )   1063 - 1069   2015.11

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    CT perfusion (CTP) is obtained cerebrovascular circulation image for assessment of stroke patients; however, at the expense of increased radiation dose by dynamic scan. Iterative reconstruction (IR) method is possible to decrease image noise, it has the potential to reduce radiation dose. The purpose of this study is to assess the visual effect of IR method by using a digital perfusion phantom. The digital perfusion phantom was created by reconstructed filtered back projection (FBP) method and IR method CT images that had five exposure doses. Various exposure dose cerebral blood flow (CBF) images were derived from deconvolution algorithm. Contrast-to-noise ratio (CNR) and visual assessment were compared among the various exposure dose and each reconstructions. Result of low exposure dose with IR method showed, compared with FBP method, high CNR in severe ischemic area, and visual assessment was significantly improvement. IR method is useful for improving image quality of low-dose CTP.

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  • Pupil size measurement and sucrose ingestion for quantifying and decreasing burden of women during mammography Reviewed International journal

    Yongbum Lee, Mieko Uchiyama, Tomoko Sumiyoshi

    Journal of Biomedical Science and Engineering   8 ( 10 )   700 - 706   2015.10

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  • Quantitative analysis of pain during mammography using electrical stimulation Reviewed

    Mieko Uchiyama, Yongbum Lee, Utako Shimizu, Rika Saitoh

    Open Journal of Nursing   5   784 - 789   2015.9

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  • Assessment of pain during mammography by a device for quantitative analysis of perception and pain Reviewed

    Mieko Uchiyama, Yongbum Lee

    Proc. of XXI IMEKO World Congress   2015.8

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  • Measurement of pupil size during mammography Reviewed

    Yongbum Lee, Mieko Uchiyama, Tomoko Sumiyoshi, Takeshi Hara

    Proc. of XXI IMEKO World Congress   2015.8

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  • The effect of humorous stimuli on alleviating pain during mammography: a preliminary study Reviewed International journal

    Yongbum Lee, Mieko Uchiyama

    Health   7 ( 6 )   659 - 664   2015.6

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  • Dose reduction by the use of a wavelet-based denoising method for digital radiography Reviewed International journal

    Haruyuki Watanabe, Du-Yih Tsai, Yongbum Lee, Eri Matsuyama

    Health   7 ( 2 )   220 - 230   2015.2

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  • Quantification of the pain and physical burden experienced during positioning for craniocaudal imaging in mammography, evaluated by measurement of muscle activity, Reviewed International journal

    Mieko Uchiyama, Yongbum Lee, Kiyoko Kazama, Yasuko Minagawa, Masaki Tsurumaki

    Health   7 ( 7 )   23 - 34   2015.1

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  • 単純CT画像におけるアルツハイマー病診断支援システムの開発

    高橋規之, 大村知己, 木下俊文, 李鎔範, 松山江里, 豊島英仁, 蔡篤儀

    日本放射線技術学会雑誌   70 ( 9 )   1060 - 1060   2014.9

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  • Quality Assessment of X-ray Images Using Modulation Transfer Function Fractal Dimension Reviewed

    Endo Shun, Lee Yongbum, Tsai Du-Yih, Hara Takeshi

    Jpn. J. Radiol. Technol.   70 ( 3 )   250 - 253   2014.3

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    Objective evaluation, e.g., using the modulation transfer function (MTF) or the noise power spectrum (NPS) of an X-ray imaging system may not correctly correspond to the subject's evaluation, e.g., when using the receiver operating characteristic (ROC). Moreover, it is difficult to measure MTF or NPS from clinical images. We therefore applied MTF fractal dimension to an X-ray imaging system. The MTF fractal dimension includes the frequency properties of the human eye in addition to quantitative complexity. In this study, we demonstrated a relationship between basic image quality and MTF fractal dimension using simulated and actual X-ray images.

    DOI: 10.6009/jjrt.2014_JSRT_70.3.250

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  • A Detection Method for Blurred Regions in Radiographs Reviewed

    Muroi Tomoya, Lee Yongbum, Tsai Du-Yih, Tsurumaki Masaki

    Jpn. J. Radiol. Technol.   70 ( 3 )   254 - 257   2014.3

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    In this paper, we propose a detection method for blurred regions in radiographs. The method involves edge detection using a Sobel filter, manually determining the region of interest (ROI), feature calculation, and classification using a support vector machine. We applied our method to 14 phantom images (7 normal images, 7 blurred images) and 14 clinical images (12 normal images, 2 blurred images). As a result, the average classification accuracies of ROIs with blurring and ROIs without blurring were 98% and 90% for phantom images and clinical images, respectively.

    DOI: 10.6009/jjrt.2014_JSRT_70.3.254

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  • Application of Edge Method for Spatial Resolution in Nuclear medicine Image Reviewed

    6 ( 6 )   7 - 12   2014.3

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  • An automated detection method for the MCA dot sign of acute stroke in unenhanced CT Reviewed

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Eri Matsuyama, Toshibumi Kinoshita, Kiyoshi Ishii

    Radiological Physics and Technology   7 ( 1 )   79 - 88   2014

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    DOI: 10.1007/s12194-013-0234-1

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  • Evaluation of physical and psychological burden of subjects in mammography Reviewed

    Yongbum Lee, Mieko Uchiyama

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   8539   508 - 513   2014

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    DOI: 10.1007/978-3-319-07887-8_71

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  • 頭部CT画像における側脳室下角の容積測定法の開発

    高橋規之, 李鎔範, 蔡篤儀, 大村知己, 松山江里, 伊藤道明, 工藤泰

    日本放射線技術学会雑誌   69 ( 9 )   1029 - 1029   2013.9

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  • Effects of mammography positioning on the autonomic nervous function Reviewed International journal

    Mieko Uchiyama, Yongbum Lee, Mieko Sadakata, Du-Yih Tsai, Mitsuko Sayama

    Health   5 ( 8 )   1335 - 1341   2013.8

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  • A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising Reviewed

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Masaki Tsurumaki, Noriyuki Takahashi, Haruyuki Watanabe, Hsian-Min Chen

    JOURNAL OF DIGITAL IMAGING   26 ( 4 )   748 - 758   2013.8

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  • A method for mammographic image denoising based on hierarchical correlations of the coefficients of wavelet transforms

    E. Matsuyama, D. Y. Tsai, Y. Lee, H. Watanabe, K. Kojima

    IFMBE Proceedings   39 IFMBE   872 - 875   2013.4

  • Automated Detection of Chest Radiograph's Region in Camera Images Reviewed

    LEE Yongbum, YOSHIDA Yuriko, TSAI Du-Yih

    The IEICE transactions on information and systems (Japanese edetion)   J96-D ( 4 )   901 - 903   2013.4

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  • Comparison of a discrete wavelet transform method and a modified undecimated discrete wavelet transform method for denoising of mammograms Reviewed

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Noriyuki Takahashi

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS   3403 - 3406   2013

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    DOI: 10.1109/EMBC.2013.6610272

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  • Efficacy of a combined wavelet shrinkage method for low-dose and high-quality digital radiography Reviewed

    H. Watanabe, D. Y. Tsai, Y. Lee, E. Matsuyama, K. Kojima

    IFMBE Proceedings   39   888 - 891   2013

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  • Improving image quality of digital mammographic images using an undecimated discrete wavelet transform method: Performance assessment

    Eri Matsuyama, Du Yih Tsai, Yongbum Lee, Haruyuki Watanabe, Masaki Tsurumaki, Katsuyuki Kojima

    20th IMEKO World Congress 2012   1   68 - 71   2012.12

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  • Improvement of image quality and radiation dose reduction in digital radiography using an integrated wavelet-transform-based method

    Haruyuki Watanabe, Du Yih Tsai, Yongbum Lee, Eri Matsuyama, Katsuyuki Kojima

    20th IMEKO World Congress 2012   1   60 - 63   2012.12

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  • Quantitative assessment of muscle activity in mammography positioning Reviewed

    Mieko Uchiyama, Yongbum Lee, Du-Yih Tsai, Kiyoko Kazama, Yasuko Minagawa, Mieko Sadakata, Mitsuko Sayama

    Proc. of XX IMEKO World Congress, TC13-P-1   338   1 - 4   2012.9

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  • Measurement of Muscle Activities for Evaluating Physical Burden and Pain during Mammography Positioning Reviewed

    Mieko Uchiyama, Yongbum Lee, Mieko Sadakata, Mitsuko Sayama, Du-Yih Tsai

    TOHOKU JOURNAL OF EXPERIMENTAL MEDICINE   228 ( 1 )   53 - 58   2012.9

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  • Computerized detection of lung nodules by CT for radiologic technologists in preliminary screening Reviewed

    Yongbum Lee, Du-Yih Tsai, Hiroshi Hokari, Yasuko Minagawa, Masaki Tsurumaki, Takeshi Hara, Hiroshi Fujita

    Radiological Physics and Technology   5 ( 2 )   123 - 128   2012.7

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  • Improvement of image quality in digital radiography by use of a combined wavelet transform method

    WATANABE Haruyuki, TSAI Du-Yih, LEE Yongbum, MATSUYAMA Eri, KOJIMA Katsuyuki

    IEICE technical report.   111 ( 389 )   269 - 274   2012.1

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    This study presents an improved wavelet-transform-based method for offering a possibility to reduce the radiation dose while maintaining a clinically acceptable image quality. The proposed method integrates a straightforward extension of our previously proposed wavelet-coefficient weighted method and the existing BayesShrink thresholding method. Experimental results demonstrated that the proposed method could improve the resolution characteristic while keeping the noise level within an acceptable limit. Our visual evaluation also showed that an approximately 50% reduction in exposure dose could be achieved with the proposed method in hip joint and lumbar spine radiographs.

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  • Computer-aided detection scheme for identification of hypoattenuation of acute stroke in unenhanced CT Reviewed

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Toshibumi Kinoshita, Naoki Ouchi, Kiyoshi Ishii

    Radiological Physics and Technology   5 ( 1 )   98 - 104   2012.1

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    DOI: 10.1007/s12194-011-0143-0

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  • Quantitative assessment of muscle activity in mammography positioning

    Mieko Uchiyama, Yongbum Lee, Du-Yih Tsai, Kiyoko Kazama, Yasuko Minagawa, Mieko Sadakata, Mitsuko Sayama

    20th IMEKO World Congress 2012   1   50 - 53   2012

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  • A concept of portable CAD and development of its fundamental techniques

    Yuriko Yoshida, Yongbum Lee, Du-Yih Tsai

    Proc. of 11th Asia Oceania Congress of Medical Physics   Yong4_1 - 4   2011.10

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  • Denoising of mammographic images using hierarchical correlations of the coefficients of discrete stationary wavelet transforms

    松山江里, TSAI Du‐Yhi, LEE Yongbum, 渡部晴之, 弦巻正樹, 高田敦子

    電子情報通信学会技術研究報告   111 ( 127(MI2011 32-46) )   31 - 36   2011.7

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    In this report, we propose an effective denoising method to attempt to denoise mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information was used as an evaluation measure in the present study. The results show that the proposed method has the potential to effectively reduce noise while maintaining high-frequency information.

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  • A Preliminary Study on Measurement of Muscle Activity during Mammography Positioning Reviewed

    UCHIYAMA Mieko, LEE Yongbum, KAZAMA Kiyoko, MINAGAWA Yasuko, TSAI Du-Yih, SADAKATA Mieko, SAYAMA Mitsuko

    Jpn. J. Radiol. Technol.   67 ( 6 )   679 - 682   2011.6

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    This study focused on measuring examinees' muscle activities during mammography positioning using surface electromyography. Muscle activities were measured in three women (40–50s) in two-view mammography (MLO: mediolateral oblique, CC: craniocaudal). The muscles measured were the sternocleidomastoid, biceps, trapezius, and gastrocnemius, selected based on the visual analogue scale reported by Sharp et al. We used a multi-purpose portable bio-amplifier (Polymate AP1000) to assess the muscle activities. The results showed that the trapezius in right MLO and sternocleidomastoid in right CC were active in all three subjects. This suggests that the muscles directly related to mammography positioning are highly active. In addition, the gastrocnemius was active throughout the mammography. The biceps and gastrocnemius were also active in at least one of the three women. We believe that quantitative assessment of muscle activities during mammography positioning will contribute to the improvement of pain-reduction programs in mammography.

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  • An Integrated Method of Wavelet Coefficient Thresholding for Reducing Radiation Dose While Maintaining Diagnostic Image Quality Reviewed

    WATANABE Haruyuki, TSAI Du-Yih, LEE Yongbum, MATSUYAMA Eri, KOJIMA Katsuyuki

    Medical Imaging and Information Sciences   28 ( 2 )   51 - 56   2011.5

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    In this paper, we investigate the effect of the use of wavelet transform for image processing on radiation dose reduction in computed radiography(CR), by measuring various physical characteristics of the wavelet-transformed images. Moreover, we propose a wavelet-based method for offering a possibility to reduce radiation dose while maintaining a clinically acceptable image quality. The proposed method integrates the advantages of two previously proposed techniques, <i>i.e.</i>, sigmoid-type transfer curve for wavelet coefficient weighting adjustment technique, as well as a wavelet soft-thresholding technique. The former can improve contrast and spatial resolution of CR images, the latter is able to decrease image noise. In the investigation of physical characteristics, we measured and compared the modulation transfer function, noise power spectrum, and contrast-to-noise ratio of CR images obtained by the proposed method and other different methods. Furthermore, visual evaluation was performed using Scheffe's pair comparison method. Experimental results showed that the proposed method could improve overall image quality as compared to other methods. Our visual evaluation showed that an approximately 40% reduction in exposure dose might be achieved in hip joint radiography by using the proposed method.

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  • ウェーブレット変換で強調した乳房X線画像に対する二点嗜好法による視覚評価

    李鎔範, 蔡篤儀

    新潟大学医学部保健学科紀要   10 ( 1 )   29 - 36   2011.2

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  • Z-score Mapping for Extracting Hypoattenuation Regions of Hyper Acute Stroke in Unenhanced CT Invited Reviewed

    TAKAHASHI Noriyuki, LEE Yongbum, TSAI Du-Yih, KINOSHITA Toshibumi, ISHII Kiyoshi

    Medical Imaging Technology   29 ( 1 )   17 - 22   2011.1

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    This paper described a z-score mapping method on the basis of a voxel-by-voxel analysis to visualize hypoattenuation regions of hyperacute stroke on unenhanced CT images. The algorithm of this method consisted of 5 major steps, <I>i.e</I>., anatomic standardization, construction of a normal reference database, calculation of z score, elimination of false positive areas, and extraction of hypoattenuation areas. The performance of this method in detection of hypoattenuation with a series of 21 patients with infarction (< 3 hours) showed the AUC =0.834 for distinction between hypoattenuation and normal regions. The result of an observer study with five neuroragiologists, also, demonstrated the average AUC for detection oh hypoattenuation regions significantly improved from 0.883 to 0.925 (<I>P</I>< 0.01). Therefore, the z-score mapping method could effectively extract hypoattenuation regions that showed a high z-score value on the z-score map. The use of this method has the potential to help neuroradiolgists detect hypoattenuation regions of hyperacute stroke on unenhanced CT images.

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  • Quantitative images quality evaluation of digital medical imaging systems using mutual information Reviewed

    Du-Yih Tsai, Eri Matsuyama, Yongbum Lee

    Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011   3   1515 - 1519   2011

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  • Radiation Dose Reduction in Digital Radiography Using Wavelet-Based Image Processing Methods Reviewed

    Haruyuki Watanabe, Du-Yih Tsai, Yongbum Lee, Eri Matsuyama, Katsuyuki Kojima

    MEDICAL IMAGING 2011: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT   7966   79661T   2011

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  • Radiation dose reduction in digital radiography using wavelet-based image processing methods Reviewed

    Haruyuki Watanabe, Du-Yih Tsai, Yongbum Lee, Eri Matsuyama, Katsuyuki Kojima

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   7966   2011

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  • Computerized quantitative evaluation of mammographic accreditation phantom images Reviewed

    Yongbum Lee, Du-Yih Tsai, Norimitsu Shinohara

    MEDICAL PHYSICS   37 ( 12 )   6323 - 6331   2010.12

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  • Selection of Optimal Wavelet Basis Function for Denoising of Planar Nuclear Images Using Mutual Information Metric Reviewed

    MATSUYAMA Eri, TSAI Du-Yih, LEE Yongbum, FUSE Masashi, KOJIMA Katsuyuki

    Medical Imaging Technology   28 ( 5 )   371 - 380   2010.11

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    In this report, we present an image evaluation method in which mutual information (MI) is employed as a metric for selecting the optimal wavelet basis function to be used in denoising planar nuclear images, with the objective of improving image quality. The higher the MI value, the better the image quality. We initially selected eight different wavelet basis functions for investigation in the present study. Subsequently, wavelet transforms were applied to planar images for denoising by employing the universal soft-thresholding method. Finally, the MI values of the wavelet-transformed images were computed for comparison. In this study, a computer-generated 2-D grid-pattern image and phantom images produced using a standard inkjet printer served as the original images. The results for the simulation and phantom images showed the same trend of ranking in terms of MI. The images processed by dmey wavelet showed the highest MI values. To validate the usefulness of the proposed method, the standard deviation rate and edge slope ratio of the processed images were calculated and compared. The results showed that the MI value can serve as an effective criterion for selecting the optimal wavelet basis function for image denoising. The results also showed that, of the eight wavelet basis functions investigated, dmey wavelet is the optimal basis function for denoising low-contrast planar images.

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  • Investigation of Noise-Resolution Tradeoff for Digital Radiographic Imaging: A Simulation Study Reviewed

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Katsuyuki Kojima

    Journal of Software Engineering and Applications   3 ( 10 )   926 - 932   2010.10

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  • Usefulness of Z-Score Mapping for Quantification of Extent of Hypoattenuation Regions of Hyperacute Stroke in Unenhanced Computed Tomography: Analysis of Radiologists&apos; Performance Reviewed

    Noriyuki Takahashi, Du-Yih Tsai, Yongbum Lee, Toshibumi Kinoshita, Kiyoshi Ishii, Hajime Tamura, Shoki Takahashi

    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY   34 ( 5 )   751 - 756   2010.9

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  • 相互情報量による核医学画像のノイズ低減のためのWavelet基底関数の選択

    松山 江里, 蔡 篤儀, 李 鎔範, 布施 真至, 小島 克之

    MEDICAL IMAGING TECHNOLOGY   28 ( Suppl. )   1 - 9   2010.7

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  • Using mutual information to evaluate performance of medical imaging systems Reviewed International journal

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Katsuyuki Kojima

    Health   2 ( 4 )   279 - 285   2010.4

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  • コンピュータ支援診断システムの新しい応用

    李鎔範, 蔡篤儀

    新潟大学医学部保健学科紀要   9 ( 3 )   35 - 41   2010.3

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  • Reduction of Radiation Dose in Computed Radiography Using Wavelet Transform

    WATANABE Haruyuki, TSAI Du-Yih, LEE Yongbum, MATSUYAMA Eri, KOJIMA Katsuyuki

    IEICE technical report   109 ( 407 )   279 - 284   2010.1

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    In this paper, we investigated the effect of the use of wavelet transform on dose reduction in computed radiography(CR). The physical properties of the processed CR images were measured using the modulation transfer function(MTF), noise power spectrum(NPS), contrast-to-noise ratio, and peak signal-to-noise ratio. Furthermore, visual evaluation was performed by Scheffe&#039;s pair comparison method. Experimental results showed that sigmoid-type transfer curves for wavelet coefficient weighting adjustment could improve the MTF, and three soft-threshold methods could improve the NPS at all spatial frequency ranges. Moreover, our visual evaluation showed that an approximately 40% reduction in exposure dose might be achieved with the sigmoid-type in hip joint radiography.

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  • Z-score Mapping Method for Extracting Hypoattenuation Areas of Hyperacute Stroke in Unenhanced CT Reviewed

    Noriyuki Takahashi, Du-Yih Tsai, Yongbum Lee, Toshibumi Kinoshita, Kiyoshi Ishii

    ACADEMIC RADIOLOGY   17 ( 1 )   84 - 92   2010.1

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  • EFFECTS OF RADIATION DOSE REDUCTION IN DIGITAL RADIOGRAPHY USING WAVELET-BASED IMAGE PROCESSING Reviewed

    Haruyuki Watanabe, Du-Yih Tsai, Yongbum Lee, Eri Matsuyama, Katsuyuki Kojima

    13TH IMEKO TC1-TC7 JOINT SYMPOSIUM - WITHOUT MEASUREMENT NO SCIENCE, WITHOUT SCIENCE NO MEASUREMENT   238   012055_1 - 7   2010

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  • A New Concept Toward Portable Computer-aided Diagnosis System:

    LEE Yongbum, TSAI Du-Yih

    Medical Imaging and Information Sciences   27 ( 2 )   29 - 32   2010

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    In this paper, a novel application of computer-aided diagnosis (CAD) system, namely portable CAD, was proposed. As a preliminary study, we investigated differences of feature values of nodules calculated by a CAD algorithm, between original chest radiograph and camera image taken by a digital camera. Sixty cases of chest radiographs were used in the study. These radiographs were initially displayed on the monitor, and then taken by the digital camera. Then, the CAD scheme was applied to the obtained camera images. In the CAD scheme, detection of nodule candidates was performed by using matched and ring filters. Two features, area and circularity, were calculated from nodule candidate regions in radiographs and camera images, and were compared respectively. In conclusion, a close correlation between the features calculated from radiographs and camera images was verified.

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  • Evaluation of Irreversible Compressed Images in Computed Radiography Using Physical Image Quality Measures Reviewed

    WATANABE Haruyuki, TSAI Du-Yih, LEE Yongbum, NAKAMURA Tomohiro, MIYAZAKI Masanori, KURAMOCHI Yoshio, KOJIMA Katsuyuki

    Jpn. J. Radiol. Technol.   65 ( 12 )   1618 - 1627   2009.12

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    Joint photographic experts group(JPEG)and JPEG2000 are widely used as image compression algorithms in medical image database systems. Compressed images have been mainly evaluated by visual assessment on acceptable compression levels in clinical studies. However, to the best of our knowledge, little work has been done to clarify image properties based on physical analysis. In this work, investigations were made to clarify image properties based on physical analysis and to discuss the major causes of degradation related to compression ratios. The physical properties of JPEG2000-compressed and JPEG-compressed images in computed radiography(CR)were compared by measuring the characteristic curve, modulation transfer function(MTF), noise power spectrum(NPS), peak signal-to-noise ratio(PSNR), contrast-to-noise ratio(CNR), and noise equivalent quanta(NEQ). In the MTF measurement, the MTFs of JPEG at high compression ratio showed pronounced degradation at all frequencies. The NPS values of JPEG2000 tend to decrease considerably compared to that of the JPEG at all frequencies with the increase of compression ratio. Furthermore, JPEG2000 images showed higher PSNR, CNR, and NEQ values than JPEG images in the same compression ratio. In these signal-to-noise ratio measurements, good reproducibility of JPEG2000 images was achieved. Overall, JPEG2000 compressed images were far superior to JPEG compressed images. In the physical properties measured, these physical analyses are useful to comprehend physical properties for each irreversible compressed image related to compression ratios in CR.

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  • CTAにおける石灰化を伴う冠動脈領域の中心線抽出能の改善 Reviewed

    井開章博, 李鎔範, 蔡篤儀, 山本功, 松本一則

    日本放射線技術学会雑誌   65 ( 9 )   1313 - 1323   2009.9

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  • Mutual information-based evaluation of image quality with its preliminary application to assessment of medical imaging systems Reviewed

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee

    JOURNAL OF ELECTRONIC IMAGING   18 ( 3 )   033011_1 - 033011_11   2009.7

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  • Study of automated detection of pulmonary nodules in thoracic computed tomography to aid screening by radiological technologist

    Hokari Hiroshi, Lee Yongbum, Tsai Du-Yih

    Bulletin of School of Health Sciences Faculty of Medicine Niigata University   9 ( 2 )   21 - 26   2009.1

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  • An Algorithm for Automated Determination of Sagittal Imaging Plane from Positioning Images in Cervical Spinal Cord MRI

    Masaki Tsurumaki, Du-Yih Tsai, Yongbum Lee, Masaru Sekiya, Kiyoko Kazama

    IEICE Technical Report(MI2008-172, Proc. of International Fourm on Medical Imageing in Asia [IFMIA2009])   108 ( 385 )   507 - 512   2009.1

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  • Evaluation of Irreversible Compressed Images Using Physical Image Quality Measures

    Haruyuki Watanabe, Du-Yih Tsai, Yongbum Lee, Tomohiro Nakamura

    IEICE Technical Report(MI2008-183, Proc. of International Fourm on Medical Imageing in Asia [IFMIA2009])   108 ( 385 )   559 - 564   2009.1

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  • Resolution and Noise Trade-Off Analysis for Digital Radiography Using Mutual-Information Metric

    Eri Matuyama, Du-Yih Tsai, Yongbum Lee, Katsuyuki Kojima

    IEICE Technical Report(MI2008-145, Proc. of International Fourm on Medical Imageing in Asia [IFMIA2009])   108 ( 385 )   391 - 395   2009.1

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  • Computer-assisted scheme for automated determination of imaging planes in cervical spinal cord MRI

    Masaki Tsurumaki, Du-Yih Tsai, Yongbum Lee, Masaru Sekiya, Kiyoko Kazama

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   7259   2009

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  • PERFORMANCE MEASUREMENT OF MEDICAL IMAGING SYSTEMS BASED ON MUTUAL INFORMATION METRIC Reviewed

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Katsuyuki Kojima

    XIX IMEKO WORLD CONGRESS: FUNDAMENTAL AND APPLIED METROLOGY, PROCEEDINGS   1614 - 1619   2009

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  • A Computer Assisted System for Determination of Oblique Sagittal Imaging Plane in Lumbar Spine MRI Reviewed

    TSURUMAKI Masaki, LEE Yongbum, TSAI Du-Yih, SEKIYA Masaru, KAZAMA Kiyoko

    Medical Imaging and Information Sciences   25 ( 3 )   54 - 60   2008.10

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    Manual determination of an oblique sagittal imaging plane with high accuracy in lumbar spine magnetic resonance imaging(MRI)needs time, experience and expertise. In this paper we present a simple and fast setting system for assisting MRI operators in their routine work to automatically and accurately determine oblique sagittal imaging plane in lumbar spine MRI. The algorithm of the presented method consists of three major steps:(1)extraction of torso region from coronal localizer image by use of histogram analysis,(2)extraction of lumbar vertebra and spinal canal regions by use of minimization and maximization filters as well as density accumulation analysis, and(3)determination of the desired position and angle of sagittal imaging plane using Hough transform. In order to evaluate the performance of our proposed method, positions and angles of the sagittal imaging planes determined by the presented method were evaluated by five radiological technologists. Thirty healthy volunteers participated in this study. All the 5 observers confirmed that the automatically determined imaging planes of all the 30 cases were acceptable for subsequent imaging procedures.

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  • Contrast Enhancement of Medical Images Using Sigmoid-type Transfer Curves for Wavelet Coefficient Weighting Adjustment Reviewed

    LEE Yongbum, TSAI Du-Yih, SUZUKI Takao

    Medical Imaging and Information Sciences   25 ( 3 )   48 - 53   2008.10

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    Pre-processing is important for digital medical images because it can contribute to the improvement of diagnostic accuracy with medical images and computer-aided diagnosis. In this paper, a novel sigmoid-type transfer curve for wavelet coefficient weighting adjustment was proposed to improve contrast of medical images. The proposed method was applied to chest radiographs, mammograms and chest CT images, and then its performance was quantitatively evaluated with contrast improvement ratio. As a result, contrasts of all applied images were obviously improved. Compared with the conventional exponential-type transfer curve, the proposed sigmoid-type transfer curve was more effective with a statistical significance(<i>p</i><0.05).

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  • Information entropy measure for evaluation of image quality Reviewed

    Du-Yih Tsai, Yongbum Lee, Eri Matsuyama

    JOURNAL OF DIGITAL IMAGING   21 ( 3 )   338 - 347   2008.9

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  • 頭部単純CT画像における急性期脳梗塞の存在判定アルゴリズムの一提案 Reviewed

    李鎔範, 高橋規之, 蔡篤儀, 石井清

    電子情報通信学会論文誌   J91-D ( 7 )   2008.7

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  • 伝達情報量を測度とするディジタル医用画像の画質評価

    松山江里, 李鎔範, 蔡篤儀

    電子情報通信学会技術研究報告   108 ( 131 )   9 - 14   2008.7

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  • A Computer-aided Diagnosis Algorithm for Acute Stroke in Non-enhanced CT Images Using Volume Disparity of symmetric cerebral regions

    Yongbum Lee, Noriyuki Takahashi, Du-Yih Tsai, Kiyoshi Isii

    Bulletin of School of Health Sciences Faculty of Medicine Niigata University   9 ( 1 )   63 - 68   2008.3

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  • Evaluation of Physical Properties of Medical Imaging Systems Using a Metric of Transmitted Information

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Masaru Sekiya

    Bulletin of School of Health Sciences Faculty of Medicine Niigata University   9 ( 1 )   57 - 62   2008.3

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  • Improvement of detection of early CT signs in hyperacute stroke using a novel noise reduction filter

    Takahashi, N., Lee, Y., Tsai, D.Y., Ishii, K., Kamio, S.

    Nippon Hoshasen Gijutsu Gakkai zasshi   64 ( 7 )   2008

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  • Improvement of detection of hypoattenuation in acute ischemic stroke in unenhanced computed tomography using an adaptive smoothing filter Reviewed

    N. Takahashi, Y. Lee, D. -Y. Tsai, K. Ishii, T. Kinoshita, H. Tamura, M. Kimura

    ACTA RADIOLOGICA   49 ( 7 )   816 - 826   2008

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  • Physical characterization of digital radiological images by use of transmitted information metric

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Masaru Sekiya, Katsuyuki Kojima

    MEDICAL IMAGING 2008: PHYSICS OF MEDICAL IMAGING, PTS 1-3   6913   2008

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  • Physical characterization of digital radiological images by use of transmitted information metric Reviewed

    Eri Matsuyama, Du-Yih Tsai, Yongbum Lee, Masaru Sekiya, Katsuyuki Kojima

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   6913   2008

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  • Adaptive partial median filter for early CT signs of acute cerebral infarction Reviewed

    Yongbum Lee, Noriyuki Takahashi, Du-Yih Tsai, Kiyoshi Ishii

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   2 ( 2 )   105 - 115   2007.8

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  • Development of an Algorithm for the Detection of Early Signs of Cerebral Ischemia on CT Images Reviewed

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Kiyoshi Ishii

    Japanese Journal of Radiological Technology   63 ( 8 )   835 - 842   2007.8

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    This paper describes an approach to automatically detect the parenchymal hypoattenuation of hyperacute stroke in nonenhanced computed tomography (CT) images. This technique is based on a comparison between the CT values histograms of right and left brains. A subtraction curve that was regarded as an output value was calculated by subtracting the right-hemispherical histogram from the left-hemispherical histogram obtained from one of the region-of-interest (ROI) sets on an image. The output value was used to assess whether hypoattenuation exists on CT images with a threshold value. If judged abnormal, a rectangular region including a whole or partial hypoattenuation area was detected from the ROI. Twenty-six cases with hypoattenuation and 30 cases without hypoattenuation were included in this study. As a result of our experiments, the sensitivity of this method in detecting hypoattenuation was found to be 92%, with approximately 0.16 false-positive per image. Our preliminary experimental results indicated that the proposed technique can be used for the automated detection of parenchymal hypoattenuation of hyperacute stroke on nonenhanced CT images. [Article in Japanese]

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  • Novel noise reduction filter for improving visibility of early computed tomography signs of hyperacute stroke: Evaluation of the filter's performance - Preliminary clinical experience Reviewed

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Kiyoshi Ishii

    Radiation Medicine - Medical Imaging and Radiation Oncology   25 ( 5 )   247 - 254   2007.6

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    DOI: 10.1007/s11604-007-0129-3

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  • Simulation study of radiographic image quality measurement based on transmitted information Reviewed

    Lee, Y., Tsai, D.Y., Matsuyama, E.

    Nippon Hoshasen Gijutsu Gakkai zasshi   63 ( 3 )   341 - 344   2007.3

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  • 医用画像の圧縮率と基本的なコンピュータ支援診断手法の性能との相関関係

    李鎔範, 蔡篤儀

    新潟大学医学部保健学科紀要   8 ( 3 )   35 - 41   2007.1

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  • 高率にエッジを保存可能な適応型部分メディアンフィルタ Reviewed

    李鎔範, 高橋規之, 蔡篤儀

    電子情報通信学会論文誌   J89-D(12)   2771 - 2775   2006.9

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  • Simple method to enhance CT brain images for use in diagnosis of acute cerebral artery infarction Reviewed

    Du-Yih Tsai, Noriyuki Takahashi, Yongbum Lee, Katsuyuki Kojima

    Proc. of 18th International Measurement Confederation (IMEKO) World Congress   TC13-02   1 - 5   2006.9

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  • A Novel Adaptive Partial Median Filter for Detectability Improvement of Acute Stroke Signs on Brain CT Images

    LEE Yongbum, TAKAHASHI Noriyuki, TSAI Du-Yih

    IEICE technical report   106 ( 75 )   19 - 24   2006.5

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    Computed tomography (CT) is an important modality for diagnosis of hyper-acute stroke. In this study, a novel adaptive partial median filter (APMF) that aimed to improve detectability of hyper-acute stroke on non-enhanced brain CT images was proposed. The APMF can reduce noise components while preserving signal components. Adequate parameters of APMF for detectability improvement of hyper-acute stroke were determined by simulation, and then, APMF were applied to clinical images. The results showed that the detectability of hyper-acute stroke were much improved.

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  • An Adaptive Partial Averaging Filter Considering Selection of Edge Components Reviewed

    TAKAHASHI Noriyuki, LEE Yongbum, TSAI Du-Yih

    The IEICE transactions on information and systems   J89-D(4) ( 4 )   888 - 892   2006.4

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  • Algorithm for Automatic Determination of the Sagittal Imaging Plane in Lumbar MRI Reviewed

    TSURUMAKI MASAKI, LEE YONGBUM, TSAI DU-YIH, SEKIYA MASARU, KAZAMA KIYOKO

    Jpn. J. Radiol. Technol.   62 ( 3 )   447 - 450   2006.3

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    DOI: 10.6009/jjrt.62.447

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  • Automatic detection of nodules isolated and touched lung wall on chest X-ray CT image using localized autocorrelation features

    HIROSE Motonari, HARA Takeshi, ZHOU Xiangrong, FUJITA Hiroshi, YOKOYAMA Ryujiro, KIRYU Takuji, HOSHI Hiroaki, LEE Yongbum, TSAI Du-Yih

    IEICE technical report   105 ( 579 )   139 - 142   2006.1

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    We have been developing a computer-aided diagnosis (CAD) system for detecting lung nodules isolated and touched lung wall in chest X-ray CT images. In this paper, a new detection method was proposed by using the higher-order autocorrelation features and the Lung Wall Template Matching (LWTM). We investigate the performance of this method by the simulation study. As a result, we achieved the sensitivity of 86.3% (63/73) with 10.9 false positives (FPs) per case in 35 clinical cases.

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  • Detectability improvement of early sign of acute stroke on brain CT images using an adaptive partial smoothing filter Reviewed

    Yonbum Lee, Noriyuki Takahashi, Du-Yih Tsai, Hiroshi Fujita

    MEDICAL IMAGING 2006: IMAGE PROCESSING, PTS 1-3   6144   2138 - 2145   2006

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  • Automatic determination of the imaging plane in lumbar MRI Reviewed

    Tsurumaki Masaki, Yongbum Lee, Du-Yih Tsai, Masaru Sekiya, Kiyoko Kazama

    MEDICAL IMAGING 2006: IMAGE PROCESSING, PTS 1-3   6144   1252 - 1269   2006

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  • Detectability improvement of early sign of acute stroke on brain CT images using an adaptive partial smoothing filter Reviewed

    Yongbum Lee, Noriyuki Takahashi, Du-Yih Tsai, Hiroshi Fujita

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   6144   2006

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  • Computer-aided diagnosis for acute stroke in nonenhanced CT Reviewed

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Kiyoshi Ishii

    International Journal of Computer Assisted Radiology and Surgery   1 ( 7 )   467   2006

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    DOI: 10.1007/s11548-006-0031-y

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  • Automatic determination of the imaging plane in lumbar MRI Reviewed

    Tsurumaki Masaki, Yongbum Lee, Du-Yih Tsai, Masaru Sekiya, Kiyoko Kazama

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   6144   2006

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  • Improvement in Visibility and Detectability of Early Sign of Acute Stroke in Nonenhanced CT Images by Using an Adaptive Partial Smoothing Filter Reviewed

    TAKAHASHI NORIYUKI, LEE YONGBUM, TSAI DU YIH, ISHII KIYOSHI, KAMIO SOUICHIRO

    Japanese Journal of Radiological Technology   61 ( 11 )   1531 - 1541   2005.11

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    Detection of early infarct signs on nonenhanced CT is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early infarct sign, which affects decisions on thrombolytic therapy. However, its detection is difficult, since early infarct sign is of subtle hypoattenuation. To improve the detectability of early infarct sign, image processing that could reduce local noise while preserving edges is desirable. To examine this issue, we devised an adaptive partial smoothing filter (APSF). Since the APSF markedly improves visibility of the normal gray-white matter interface, loss of the gray-white matter interface due to hypoattenuation could be more easily detected. The APSF was applied to clinical CT images in hyperacute stroke patients. Our preliminary results showed that the visibility and detectability of early infarct signs was much improved. To validate the usefulness of the proposed method, two commonly used smoothing filters were also employed for comparison. The results demonstrated the superiority of the APSF. Our proposed APSF can improve the visibility of the gray-white matter interface, thereby enhancing the detectability of early infarct signs.

    DOI: 10.6009/jjrt.KJ00004010674

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  • MTFの概念に基づく基礎的なウェーブレット画像圧縮パラメータの決定法 Reviewed

    李鎔範, 蔡篤儀, 井開章博

    Medical Imaging Technology   23 ( 4 )   239 - 243   2005.9

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    DOI: 10.11409/mit.23.239

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  • Intelligent Computer-Aided Diagnosis Based on Normal Structure Recognition of Human Body

    Hiroshi Fujita, Takeshi Hara, Xiangrong Zhou, Takuji Kiryu, Masayuki Kanematsu, Toshio Goto, Hiraoka Hoshi, Yongbum Lee, Du-Yih Tsai, Daisuke Fukuoka, Yuji Hatanaka, Tomoko Matsubara, Tokiko Endo

    Proc. of the First International Symposium on Intelligent Assistance in Diagnosis of Multi-Dimensional Medical Images   1   42 - 45   2005.3

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  • An Improved Adaptive Neighborhood Contrast Enhancement Method for Medical Images Reviewed

    Du-Yih Tsai, Yongbum. Lee, Reina Chiba

    Proc. of the Third IASTED International Conference on Biomedical Engineering (BioMED2005)   1   59 - 63   2005.2

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  • An Automated Bone Removal Technique for Extraction of Cerebral Vessels from Head CT Angiography Reviewed

    Yongbum Lee, Du-Yih Tsai, Satomi Inomata, Ryujiro Yokoyama, Takeshi Hara, Hiroshi Fujita, Masayuki Kanematsu, Toru Iwama, Hiroaki Hoshi

    Proc. of the Third IASTED International Conference on Biomedical Engineering (BioMED2005)   1   22 - 25   2005.2

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  • Improvement in the Performance of the Adaptive Neighborhood Contrast Enhancement Technique Based on Entropy Reviewed

    CHIBA REINA, LEE YONGBUM, TSAI DU YIH

    Japanese Journal of Radiological Technology   61 ( 2 )   268 - 276   2005.2

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    This paper presents an improved adaptive-neighborhood-contrast-enhancement (ANCE) method for the improvement of medical image quality. The ANCE method consists of computing the local contrast around each pixel using a variable neighborhood whose size depends on the statistical properties around the given pixel. The obtained contrast image is then transformed into a new contrast image using a contrast enhancement function. Finally, a contrast-enhanced image is obtained by applying inverse contrast transform to the previous step. This technique provides the advantages of enhancing or preserving image contrast while suppressing noise. However, it does have a drawback. The performance of the ANCE method largely depends on how to determine the parameters used in the processing steps. The present study proposes a novel method for optimal and automatic determination of several parameters using entropy. To quantitatively compare the performance of the proposed method with that of the ANCE method, computer-simulated images are generated. The output-to-input SNR level and the mean squared error are used as comparison criteria. Results demonstrated the superiority of the proposed method. Moreover, we have applied our new algorithm to echocardiograms and mammograms. Our results showed that the proposed method has the potential to become useful for improving the image quality of medical images.

    DOI: 10.6009/jjrt.KJ00003326665

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  • 高次局所自己相関特徴とGAテンプレートマッチングを用いた胸部X線CT画像における結節状陰影の自動検出 Reviewed

    廣瀬元就, 原武史, 周向栄, 藤田広志, 桐生拓司, 横山龍二郎, 星博昭, 李鎔範, 蔡篤儀

    電子情報通信学会技術研究報告   104 ( 580 )   115 - 118   2005.1

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  • An adaptive enhancement algorithm for CT brain images Reviewed

    Du-Yih Tsai, Noriyuki Takahashi, Yongbum Lee

    2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-7   1   3398 - 3401   2005

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  • CT画像における急性期脳梗塞の描出能の改善(画像工学 画像処理・ノイズ解析, 一般研究発表予稿集, 日本放射線技術学会第33回秋季学術大会)

    高橋 規之, 神尾 總一郎, 李 鎔範, 蔡 篤儀

    日本放射線技術学会雑誌   61 ( 9 )   1221 - 1221   2005

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    DOI: 10.6009/jjrt.KJ00004028181

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  • Medical image classification using genetic-algorithm based fuzzy-logic approach Reviewed

    DY Tsai, Y Lee, M Sekiya, M Ohkubo

    JOURNAL OF ELECTRONIC IMAGING   13 ( 4 )   780 - 788   2004.10

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    DOI: 10.1117/1.1786607

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  • CT画像系におけるline spread function (LSF)およびslice sensitivity profile (SSP)とpoint spread function (PSF)の関連 Reviewed

    大久保真樹, 和田真一, 小林悌二, 李鎔範, 蔡篤儀

    医学物理   24 ( 3 )   115 - 122   2004.9

  • Preliminary Study on Automated Detection of Cerebral Vessels from Head CTA Images Reviewed

    INOMATA SATOMI, LEE YONGBUM, TSAI DU-YIH, YOKOYAMA RYUJIRO, HARA TAKESHI, FUJITA HIROSHI, KANEMATSU MASAYUKI, IWAMA TORU, HOSHI HIROAKI

    Japanese Journal of Radiological Technology   60 ( 9 )   1325 - 1331   2004.9

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    We propose an approach for automated detection of cerebral vessels from head CT angiographic images. This approach contains two major features. First, instead of using the well-known image-processing techniques such as thresholding and labeling, a novel Laplacian-like filter is developed and employed in the region of interest in an image to be processed. Second, not only is the axial-view image reconstructed from head CT angiographic images used, but, in addition, the sagittal-and coronal-view images are reconstructed and used. By applying these major features in the process of detection of brain vessels, more accurate results can be achieved. To validate the effectiveness of the proposed method, we applied the method to three clinical cases, all of which were head CT angiograms. Our preliminary results showed that the proposed method has the potential to automatically detect cerebral vessels in head CT angiograms with acceptable accuracy.

    DOI: 10.6009/jjrt.KJ00003174600

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  • Inteligent CAD based on understanding normal anatomical structure - Purpose of our study and future plan -

    Hiroshi Fujita, Takeshi Hara, Xiangrong Zhou, Takuji Kiryu, Masayuki Kanematsu, Toshio Goto, Hiroaki Hoshi, Yongbum Lee, Du-Yih Tsai, Daisuke Fukuoka, Yuji Hatanaka, Tomoko Matsubara, Tokiko Endo

    Intelligent Assistance in Diagnosis of Multi-dimensional Medical Images   .   69 - 74   2004.3

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  • ROC Comparison of Classification Schemes Based on Artificial Neural Network and Fuzzy Logic for Mammographic Microcalcifications Reviewed

    LEE Yongbum, TSAI Du-Yih

    Medical Imaging and Information Sciences   21 ( 1 )   122 - 130   2004.1

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    The purpose of this study is to compare four CAD-algorithms under the same condition, and to verify the most effective algorithm for the classification of microcalcifications on mammogram. The four CAD-algorithms are BP-ANN, GA-ANN, FL and GA-FL. BP-ANN is a conventional artificial neural network with back-propagation learning. GA-ANN is an improved ANN based on genetic algorithm to determine the weighting coefficients at ANN. FL is a conventional fuzzy logic algorithm using gaussian-distributed membership functions. GA-FL is an improved FL based on genetic algorithm to optimize the membership functions. Comparison results are indicated by ROC curves. The Az values of the ROC curves for BP-ANN, GA-ANN, FL and GA-FL were 0.86, 0.80, 0.89 and 0.95, respectively. When sensitivity of each algorithm was 100%, specificities of BP-ANN, GA-ANN, FL and GA-FL were 69%, 54%, 31% and 77%, respectively. Therefore, these results show the effectiveness of GA-FL algorithm. GA-FL has a potential to become useful CAD algorithm to classify microcalcifications on mammogram.

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  • Relationship between line spread function (LSF), or slice sensitivity profile (SSP), and point spread function (PSF) in CT image system

    Ohkubo, M., Wada, S., Kobayashi, T., Lee, Y., Tsai, D.-Y.

    Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics   24 ( 3 )   2004

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  • Improved adaptive neighborhood pre-processing for medical image enhancement Reviewed

    DY Tsai, Y Lee

    COMPUTATIONAL AND INFORMATION SCIENCE, PROCEEDINGS   3314 ( 4 )   576 - 581   2004

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  • Improvement in automated detection of pulmonary nodules on helical x-ray CT images Reviewed

    Y Lee, DY Tsai, T Hara, H Fujita, S Itoh, T Ishigaki

    MEDICAL IMAGING 2004: IMAGE PROCESSING, PTS 1-3   5370   824 - 832   2004

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    DOI: 10.1117/12.536162

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  • Computerized classification of microcalcifications on mammograms using fuzzy logic and genetic algorithm Reviewed

    YB Lee, DY Tsai

    MEDICAL IMAGING 2004: IMAGE PROCESSING, PTS 1-3   5370   952 - 959   2004

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    DOI: 10.1117/12.536274

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  • 259 CT画像における急性期脳梗塞の自動検出法の検討(画像工学 CAD 脳)(一般研究発表)(第32回秋季学術大会)

    高橋 規之, 神尾 總一郎, 李 鎔範, 蔡 篤儀

    日本放射線技術学会雑誌   60 ( 9 )   1250 - 1250   2004

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    DOI: 10.6009/jjrt.KJ00003174512

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  • 診療放射線技師国家試験対策のための模擬試験用Webデータベースシステムの構築

    李鎔範

    新潟大学医学部保健学科紀要   7 ( 5 )   669 - 675   2003.12

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  • 遺伝的アルゴリズムを適用したファジィ推論による微小石灰化像の良悪性鑑別法 Reviewed

    李鎔範, 蔡篤儀, 関谷勝

    生体医工学   41 ( 2 )   19 - 28   2003.6

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  • An Integrated Fuzzy-GA-Based CAD System for Disease Discriminations Reviewed

    Du-Yih Tsai, Yongbum Lee, Masaru Sekiya, Masaki Ohkubo, Katsuyuki Kojima, Isao Yamada

    Proc. of the IASTED International Conference on Biomedical Engineering   166 - 171   2003.6

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  • Automated classification of mammographic microcalcifications based on fuzzy logic applying a genetic algorithm

    Lee, Y., Tsai, D.-Y., Sekiya, M.

    Japanese Journal of Medical Electronics and Biological Engineering   41 ( 2 )   2003

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  • A method of medical image enhancement using wavelet-coefficient mapping functions Reviewed

    DY Tsai, Y Lee

    PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2   2   1091 - 1094   2003

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  • Computerized Classification of Clustered Microcalcifications on Mammograms Reviewed

    Yongbum Lee, Du-Yih Tsai, Masaru Sekiya

    Proc. of the Second IASTED International Conference on Visualization, Imaging, and Imaging Processing   1   406 - 411   2002.9

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  • Digital Chest Image Enhancement Using Wavelet-Coefficient Mapping Functions Reviewed

    Du-Yih Tsai Yongbum Lee, Masaru Sekiya, Katsuyuki Kojima

    Proceedings of the Second IASTED International Conference on Visualization, Imaging, and Imaging Processing   1   324 - 329   2002.9

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  • An Automated Detection of Lacunar Infarct Regions in Brain MR Images : Preliminary Study Reviewed

    YOKOYAMA RYUJIRO, LEE YONGBUM, HARA TAKESHI, FUJITA HIROSHI, ASANO TAKAHIKO, HOSHI HIROAKI, IWAMA TORU, SAKAI NOBORU

    Japanese Journal of Radiological Technology   58 ( 3 )   399 - 405   2002.3

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    The purpose of this study is to develop a technique to detect lacunar infarct regions automatically in brain MR images. Our detection method is based on the definition of lacunar infarcts. After inputted images were binarized, we used feature values such as area, circularities and the center of gravity of candidate regions to extract isolated lacunar infarct regions. We also developed and used a new filter to enhance the signals of lacunar infarcts adjacent to some high intensity regions. 10 cases involving 81 sectional images were applied to our experiment. As a result, the sensitivity was 100% with approximately 1.77 false-positives per image. Our results are promising on the first stage, although it remains to improve on problems that to eliminate false-positives and automatically establish threshold value.

    DOI: 10.6009/jjrt.KJ00001364293

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  • A Preliminary Study of Wavelet-Coefficient Transfer Curves for the Edge Enhancement of Medical Images Reviewed

    Du-Yih Tsai, Yongbum Lee, Satoshi Sakaguchi

    Japanese Journal of Medical Electronics and Biological Engineering   40 ( 2 )   86 - 90   2002

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  • Fuzzy-reasoning-based computer-aided diagnosis for automated discrimination of myocardial heart disease from ultrasonic images Reviewed

    DY Tsai, Y Lee

    ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE   85 ( 11 )   1 - 8   2002

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  • A method of medical image enhancement using wavelet analysis Reviewed

    DY Tsai, YB Lee, M Sekiya, S Sakaguchi, Yamada, I

    2002 6TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I AND II   1   723 - 726   2002

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  • 局所自己相関特徴を用いた胸部ヘリカルCT画像上での腫瘤陰影の認識

    李鎔範, 原武史, 藤田広志, 蔡篤儀, 伊藤茂樹, 石垣武男

    新潟大学医学部保健学科紀要   7 ( 3 )   375 - 381   2001.12

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  • Fuzzy Reasoning Based Computer-Aided Diagnosis for Automated Discrimination of Myocardial Heart Disease from Ultrasonic Images Reviewed

    TSAI Du-Yih, LEE Yongbum

    The Transactions of the Institute of Electronics,Information and Communication Engineers. A   J84-A(12) ( 12 )   1431 - 1438   2001.12

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  • Automatic Detection of Nodules on Chest X-ray CT Images Using Higher-order Autocorrelation Features Reviewed

    LEE Yongbum, NAKAGAWA Toshiaki, HARA Takeshi, FUJITA Hiroshi, ITOH Shigeki, ISHIGAKI Takeo

    18 ( 3 )   135 - 143   2001.9

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    DOI: 10.11318/mii1984.18.135

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  • A Morphology-based Method for Automated Detection of Clustered Microcalcifications Reviewed

    Du-Yih Tsai, Masaru Sekiya, Yongbum Lee, Yoshihiro Yamazaki, Masaki Ohkubo, Katsuyuki Kojima, Isao Yamada

    Proc. of IASTED International Conference Signal Processing, Pattern Recognition, and Applications   1   159 - 162   2001.7

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  • Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique Reviewed

    Y Lee, T Hara, H Fujita, S Itoh, T Ishigaki

    IEEE TRANSACTIONS ON MEDICAL IMAGING   20 ( 7 )   595 - 604   2001.7

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  • 頭部MR画像におけるラクナ梗塞領域の自動認識

    李鎔範, 横山龍二郎, 原武史, 藤田広志, 浅野隆彦, 星博昭

    電子情報通信学会技術研究報告   100 ( 596 )   123 - 126   2001.1

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  • Development of a computer-aided diagnosis system for chest x-ray helical CT images (in Japanese)

    Lee, Y.

    Medical Physics   28 ( 7 )   1536 - 1536   2001

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    DOI: 10.1118/1.1382823

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  • Fuzzy-reasoning based diagnosis scheme for automated classification of heart disease from ultrasonic images Reviewed

    DY Tsai, Y Lee

    10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3   1   188 - 191   2001

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  • Computer-aided diagnosis in abdominal and cardiac radiology using neural networks Reviewed

    DY Tsai, M Sekiya, Y Lee

    8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING   1   375 - 379   2001

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  • A Study on Development of a Computer-aided Diagnosis System for Chest X-ray Helical CT Images

    Lee Yongbum

    Japanese Journal of Radiological Technology   57 ( 9 )   1145 - 1145   2001

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    DOI: 10.6009/jjrt.KJ00003111315

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  • 胸部ヘリカルCT画像を用いたシミュレーションによるGAテンプレートマッチング法の評価 Reviewed

    李鎔範, 原武史, 藤田広志

    医用画像情報学会誌   17 ( 3 )   118 - 129   2000.12

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    DOI: 10.11318/mii1984.17.118

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  • 半円形モデルのテンプレートマッチングによる胸部ヘリカルCT画像における胸壁周辺の腫瘤陰影の自動検出法 Reviewed

    李鎔範, 児島敦司, 原武史, 藤田広志, 伊藤茂樹, 石垣武男

    電子情報通信学会論文誌   J83-D-II(1)   419 - 422   2000.12

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  • Automated detection of pulmonary nodules in helical CT images Reviewed

    Y Lee, T Hara, H Fujita, S Itoh, T Ishigaki

    CARS 2000: COMPUTER ASSISTED RADIOLOGY AND SURGERY   1214   1044 - 1044   2000

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  • Automated lesion detection methods for 2D and 3D chest X-ray images Reviewed

    Takeshi Hara, Hiroshi Fujita, Yongbum Lee, Hitoshi Yoshimura, Shoji Kido

    Proceedings - International Conference on Image Analysis and Processing, ICIAP 1999   1   768 - 773   1999

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    DOI: 10.1109/ICIAP.1999.797688

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  • Pattern recognition technique for chest CAD system Reviewed

    A Hara, H Fujita, S Goto, Y Lee, H Yoshimura, J Xu

    COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING   1182   57 - 61   1999

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  • Fundamentals of Medical 3D Images and Intelligent Information Processing

    HARA Takeshi, LEE Yongbum, FUJITA Hiroshi

    Medical Imaging Technology   16 ( 2 )   103 - 110   1998.3

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    Language:Japanese   Publisher:The Japanese Society of Medical Imaging Technology  

    DOI: 10.11409/mit.16.103

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    Other Link: http://search.jamas.or.jp/link/ui/1998177115

  • Automated pulmonary nodule detection on helical CT images Reviewed

    Y Lee, T Hara, H Fujita, A Kojima, S Itoh, T Ishigaki

    CAR '98 - COMPUTER ASSISTED RADIOLOGY AND SURGERY   1165   878 - 878   1998

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  • Nodule detection on chest helical CT scans by using a genetic algorithm Reviewed

    Y Lee, T Hara, H Fujita, S Itoh, T Ishigaki

    INTELLIGENT INFORMATION SYSTEMS, (IIS'97) PROCEEDINGS   1   67 - 70   1997

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  • Breast Peripheral-Area Enhancement Processing for Mammogram CAD by using Dynamic-Range Compression

    HARA Takeshi, LEE Yongbum, FUJITA Hiroshi

    Medical Imaging and Information Sciences   13 ( 2 )   78 - 82   1996.5

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    We are developing a computer-aided diagnostic system (CAD) which can detect masses and clustered microcalcifications on digital mammograms. In indicating the computer results to the physicians on an ordinary cathode-ray tube monitor, an automated windowing method has been applied to enhance the shadow of mammary glands. The area near the breast skin lines was difficult to evaluate, because most of pixel values in those areas were ignored by the method. Therefore, the improvement of contrast-correction process is required for the CAD. The purpose of this paper is to describe the dynamic-range compression technique which could make observers easier to recognize the regions in high and/or low density area, and is also to apply the method to the display stage of annotated images in our mammogram CAD.

    DOI: 10.11318/mii1984.13.78

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Books

  • 新・医用放射線科学講座 医療画像情報工学 第2版

    寺本篤司(編), 藤田広志(編)( Role: Contributor ,  第3編第4章6-3) X線CT画像を対象としたCAD, pp157-160)

    医歯薬出版  2023.11  ( ISBN:4263206533

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    Total pages:240   Language:Japanese

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  • 医用画像情報学 (診療放射線技術選書)

    杜下淳次(編)( Role: Contributor ,  5章 B. 情報理論,pp.186-204,F. 3次元画像処理など,pp.240-255)

    南山堂  2023.10  ( ISBN:4525279354

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    Total pages:360  

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  • 別冊医学のあゆみ バイオインフォマティクスの世界

    奥田修二郎(編)( Role: Contributor ,  画像診断用人工知能─コンピュータ支援診断(CAD),pp.66-71)

    医歯薬出版  2023.3 

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  • 2021-2022年版 標準 医用画像のためのディープラーニング実践編 (医療AIとディープラーニングシリーズ)

    原, 武史, 藤田, 広志( Role: Contributor ,  Chapter 5, Chapter 11)

    オーム社  2020.11  ( ISBN:4274226395

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    Total pages:200   Language:Japanese

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  • よくわかる 医用画像情報学

    石田隆行, 近藤世範, 小笠原克彦( Role: Edit)

    オーム社  2018.9  ( ISBN:4274221318

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    Total pages:260   Language:Japanese

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  • 臨床研究のためのディジタル画像処理の基礎とパソコンソフト活用術

    公益財団法人日本放射線技術学会( Role: Contributor)

    メディカルトリビューン  2013.4  ( ISBN:4895894258

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    Total pages:153   Language:Japanese

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  • 実践医用画像解析ハンドブック

    藤田, 広志, 石田, 隆行, 桂川, 茂彦, 原, 武史, 目加田, 慶人, 加野, 亜紀子, 羽石, 秀昭( Role: Contributor ,  第2章3.2節 画像の変形 pp.131-136,付録B pp.768-785)

    オーム社  2012.11  ( ISBN:4274212823

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    Total pages:835   Language:Japanese

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  • Computed Tomography: Clinical Applications

    Luca Saba( Role: Contributor ,  Chapter 1: Computer-Aided Diagnosis for Acute Stroke in CT Images. pp.3-28)

    2012.1  ( ISBN:9533073780

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    Total pages:356  

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  • Medical imaging

    Okechukwu, Felix Erondu( Role: Contributor ,  Chapter 9: A Mutual Information-Based Image Quality Metric for Medical Imaging Systems. pp.195-212)

    InTech  2011.12  ( ISBN:9533077743

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    Total pages:416   Language:English

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  • Lung imaging and computer-aided diagnosis

    El-Baz, Ayman, Suri, Jasjit S.( Role: Contributor ,  Chapter 12: Concept and practice of genetic algorithm template matching (GATM) and higher order local autocorrelation schemes in automated detection of lung nodules. pp.267-295)

    CRC Press  2011.8  ( ISBN:1439845573

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    Total pages:496   Language:English

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  • 医用画像ハンドブック

    石田, 隆行, 桂川, 茂彦, 藤田, 広志( Role: Contributor ,  pp.193-197)

    オーム社  2010.12  ( ISBN:4274209555

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    Total pages:1618   Language:Japanese

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MISC

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Presentations

  • 深部静脈血栓症に対する簡易診断ツールの開発 Invited

    近藤世範

    MDF2023第6回医工連携マッチング例会  2023.11 

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  • 画像診断支援AIの研究動向 Invited

    近藤世範

    第77回新潟画像医学研究会  2023.11 

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  • メディカルAIの最前線:AI-CADの研究事例紹介 Invited

    近藤世範

    第765回新潟医学会例会シンポジウム  2022.5 

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  • 医療用AI研究の動向—AI-CADを中心に— Invited

    近藤世範

    3D PACS研究会:特別講演  2022.1 

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  • Deep learningの現状と今日の目標設定

    近藤世範

    医用画像情報学会:第5~8回深層学習実践セミナー教育講演(第5回:2020.11.1、第6回:2021.7.4、第7回:2021.1.24、第8回:2021.12.12,第10回:2022.12.11)  2021.12 

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  • DX時代における整形外科バイオメカニクスの展開:AIベースのコンピュータ支援診断について Invited

    近藤世範

    第48回日本臨床バイオメカニクス学会:特別シンポジウム  2021.11 

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  • 基礎編:ディープラーニングの基礎〜これから取り組む方へ〜 Invited

    李鎔範

    第39回日本医用画像工学会:第10回JAMITチュートリアル講演会  2020.9 

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  • 医用画像診断分野におけるDeep learning の動向 Invited

    李 鎔範

    新潟県労働衛生医学協会:講演  2020.1 

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  • Deep Learningの基礎と医用画像への応用事例の紹介 Invited

    李鎔範

    新潟県診療放射線技師会平成30年度下越地区会:特別講演  2019.2 

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  • 医用画像の空間処理と空間周波数処理 Invited

    李鎔範

    日本放射線技術学会第41回秋季学術大会:入門講座7(画像工学)  2013.10 

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  • Image Processing for computer-aided diagnosis of acute stroke in unenhanced CT Invited International conference

    LEE Yongbum

    2013 Annual spring scientific congress of Korean Society of Radiological Science  2013.5 

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  • マンモグラフィCADの情勢 Invited

    李鎔範

    新潟大学大学院保健学研究科GSH研究実践センター:市民公開講座  2012.6 

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  • 直交変換の基礎と応用 Invited

    李鎔範

    日本放射線技術学会第67回総会学術大会:専門講座1(画像工学1)  2012.4 

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  • CAD(初級編) Invited

    李鎔範

    日本放射線技術学会第65回総会学術大会:教育講座  2009.4 

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  • コンピュータ支援診断の基礎知識-ImageJのプラグイン開発とRを用いた特徴量分析- Invited

    李鎔範

    日本放射線技術学会第65回総会学術大会:技術活用セミナー4  2009.4 

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  • コンピュータ支援診断(CAD)の情勢と研究事例の紹介 Invited

    李鎔範

    第5回新潟RadiologyUpdate学術講演会  2009.3 

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  • 救急医療におけるコンピュータ支援診断 Invited

    李鎔範

    第28回CT画像研究会  2008.4 

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  • CT画像における急性期脳梗塞のCAD Invited

    李鎔範

    日本放射線技術学会第64回総合学術大会:モーニングセミナー2  2008.4 

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  • 微小石灰化像良悪性鑑別のための人工ニューラルネットワーク法とファジィ推論法のROC比較評価 Invited

    李鎔範

    医用画像情報学会平成18年度年次大会(第145回大会):内田論文賞受賞記念講  2006.6 

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  • 微小石灰化像良悪性鑑別のための人工ニューラルネットワーク法とファジィ推論法のROC比較評価 Invited

    李鎔範

    医用画像情報学会平成17年度年次大会(第142回大会):金森奨励賞受賞記念講演  2005.6 

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Awards

  • Doi Award 2024

    2025.4   Radiological Physics and Technology   Deep learning-based correction for time truncation in cerebral computed tomography perfusion

    Shota Ichikawa, Makoto Ozaki, Hideki Itadani, Hiroyuki Sugimori, Yohan Kondo

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  • Best Paper Award 3rd prize

    2022.12   CARS 2022(The 36th International Conference on Computer-Assisted Radiology and Surgery)   Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT

    Yuki Sasaki, Yohan Kondo, Tadashi Aoki, Naoya Koizumi, Toshiro Ozaki, Hiroshi Seki

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  • 内田論文賞

    2020.6   医用画像情報学会  

    松山江里, 李鎔範, 高橋規之, 蔡篤儀

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  • 技術奨励賞 画像分野

    2020.4   日本放射線技術学会  

    李鎔範

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  • Certificate of Merit

    2019.12   Radiological Society of North America  

    Haruyuki Watanabe, Aya Sakaguchi, Hideyoshi Mitsutake, Kiyoshi Uchiyama, Yongbum Lee, Norio Hayashi, Masayuki Shimosegawa, Kaori Akutsu, Sae Tamura

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  • 学術奨励賞

    2012.11   日本放射線技術学会東北部会  

    李鎔範

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    Award type:Honored in official journal of a scientific society, scientific journal  Country:Japan

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  • Cum Laude

    2008.12   Radiological Society of North America  

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Kiyoshi Ishi, Toshibumi Kinoshita, Kenrou Iwaki

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    Award type:Award from international society, conference, symposium, etc.  Country:United States

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  • Certificate of Merit

    2007.11   Radiological Society of North America  

    Noriyuki Takahashi, Yongbum Lee, Du-Yih Tsai, Kiyoshi Ishi

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    Award type:Award from international society, conference, symposium, etc.  Country:United States

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  • 内田論文賞

    2006.6   医用画像情報学会  

    李鎔範, 蔡篤儀

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    Award type:Honored in official journal of a scientific society, scientific journal  Country:Japan

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  • 金森奨励賞

    2005.6   医用画像情報学会  

    李鎔範

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    Award type:Honored in official journal of a scientific society, scientific journal  Country:Japan

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  • 金森奨励賞

    2002.6   医用画像情報学会  

    李鎔範, 中川俊明, 原武史

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    Award type:Honored in official journal of a scientific society, scientific journal  Country:Japan

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  • Certificate of Merit

    2001.11   Radiological Society of North America  

    Hiroshi Fujita, Takeshi Hara, Daisuke Fukuoka, Yongbum Lee, Tomoko Matsubara

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Research Projects

  • Development of a VMAT quality assurance system based on 3D reconstruction using deep learning

    Grant number:25K11031

    2025.4 - 2028.3

    System name:Grants-in-Aid for Scientific Research

    Research category:Grant-in-Aid for Scientific Research (C)

    Awarding organization:Japan Society for the Promotion of Science

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    Grant amount:\4550000 ( Direct Cost: \3500000 、 Indirect Cost:\1050000 )

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  • 生前/死後CT画像の骨形状情報に基づく個人同定システムの開発

    Grant number:24K10832

    2024.4 - 2027.3

    System name:科学研究費助成事業

    Research category:基盤研究(C)

    Awarding organization:日本学術振興会

    近藤 世範, 高橋 直也, 近藤 達也, 市川翔太

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    Authorship:Principal investigator 

    Grant amount:\4550000 ( Direct Cost: \3500000 、 Indirect Cost:\1050000 )

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  • 抗がん剤脱毛時の発毛段階に即したスカルプケアを目標とした頭皮画像評価の策定

    Grant number:24K13827

    2024.4 - 2027.3

    System name:科学研究費助成事業

    Research category:基盤研究(C)

    Awarding organization:日本学術振興会

    内山 美枝子, 近藤 世範

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    Authorship:Coinvestigator(s) 

    Grant amount:\4680000 ( Direct Cost: \3600000 、 Indirect Cost:\1080000 )

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  • Delta-radiomicsを応用したVMAT患者個別QAシステムの開発

    Grant number:22K07792

    2022.4 - 2025.3

    System name:科学研究費助成事業 基盤研究(C)

    Research category:基盤研究(C)

    Awarding organization:日本学術振興会

    宇都宮 悟, 近藤 世範, 中野 永, 棚邊 哲史

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    Grant amount:\3770000 ( Direct Cost: \2900000 、 Indirect Cost:\870000 )

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  • ディープラーニングを用いたマンモグラム時系列解析による対側乳癌予測システムの開発

    Grant number:21K07657

    2021.4 - 2024.3

    System name:科学研究費助成事業 基盤研究(C)

    Research category:基盤研究(C)

    Awarding organization:日本学術振興会

    山室 美佳, 李 鎔範, 浅井 義行, 石井 一成

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    Grant amount:\4290000 ( Direct Cost: \3300000 、 Indirect Cost:\990000 )

    乳腺密度を軸とする対側乳癌発症リスク予測システム確立の基礎実験として、先行研究で収集済みの3000症例の生データマンモグラムについて人工知能技術を用い、乳腺密度算出のための乳腺領域を抽出するU-netモデルを作成した.
    今後、過去に遡っての画像や、他院で撮影された画像を研究に利用できるようにするため、当院の標準画像処理条件で作成した乳腺領域抽出のためのU-netモデルが、異なる画像処理を行ったマンモグラムに適用できるかどうかを評価した. 4つの異なる処理を施した画像に作成したU-netを適応して抽出された領域の乳腺密度とGround Truthを比較し、Bland-Altman解析を行った. その結果、ダイナミックレンジ圧縮を強くするとU-netモデルへの影響が顕著となり、固定誤差や比例誤差を生じる要因となるため注意が必要ではあるが、日常診療で使用される範囲内での画像処理の違いはU-netモデルの適用精度に顕著な影響を及ぼすことはなく、少なくともGround Truthとの互換性が保たれていると判断できた.
    次にRaw dataを用いず、臨床画像に付帯している情報のみで乳腺密度を推定するための予備実験を行なった. 簡易的に画素値のみで乳腺を含むと判断した領域の乳腺密度値2223例について、重回帰式を作成すると乳腺密度の全変動の90%程度を説明できることがわかった.
    これらの研究と同時に2000例程度の生データマンモグラムを追加収集した. そのうち1700例程度が対側乳癌症例であり、更にこの内7割が連続してデータ収集できている症例であった.

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  • Development of automated assessment tool of DVT risk in disaster or in home care

    Grant number:20K11068

    2020.4 - 2023.3

    System name:Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

    Research category:Grant-in-Aid for Scientific Research (C)

    Awarding organization:Japan Society for the Promotion of Science

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    Grant amount:\4290000 ( Direct Cost: \3300000 、 Indirect Cost:\990000 )

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  • 抗がん剤脱毛時の頭皮悪化が予測できるウィッグ装着型ウェアラブル端末の開発

    Grant number:19H03931

    2019.4 - 2023.3

    System name:科学研究費助成事業 基盤研究(B)

    Research category:基盤研究(B)

    Awarding organization:日本学術振興会

    内山 美枝子, 近藤 世範, 峰松 健夫, 大貝 和裕, 飯島 淳彦, 黒瀬 雅之, 玉井 奈緒, 小山 諭, 坂上 百重, 横野 知江, 柏 美智, 奥田 明子, 坂井 さゆり

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    Grant type:Competitive

    Grant amount:\17030000 ( Direct Cost: \13100000 、 Indirect Cost:\3930000 )

    本研究は、抗がん剤投与における頭皮の炎症レベルとの関連を見つけるために、①抗がん剤投与による頭皮炎症レベルの決定と②炎症レベルを教師データとし、入力データに頭皮状態の測定結果を採用した機械学習(人工知能の1つ)を、新潟大学ビックデータアクティベーションにて行うことで医学的なエビデンスをつけた抗がん剤投与中の頭皮の炎症レベルと頭皮状態の変化シグナルと関連を提示することを目的とし、時系列的に悪化が予測できるウィッグ装着型のウェアラブル端末の開発を最終目標としている。抗がん剤脱毛を有するがん患者が頭皮のかゆみや不快感を訴えるのは、梅雨時期などの外気湿度の上昇時や夏季の高温時、冬季の外気湿度の低い乾燥時といった外部環境が要因となる場合と、動作に伴う発汗時にその訴えが顕著になる。これは皮膚炎と同様に、抗がん剤投与による頭皮のバリア機構の低下に伴う外部刺激に対する感度の上昇が主たる要因である。その実現のためには、①抗がん剤投与中の頭皮から、頭皮状態の変化シグナルを検出できるか?②抗がん剤投与中の頭皮の炎症レベルは頭皮状態の変化シグナルと関連があるか?の2つの問いを明確にする必要がある。
    その検証のため2019年度から実施している抗がん剤脱毛の頭皮の形態および状態の検証(①人工気候室を用いた環境条件における頭部環境の検証と実験②頭皮の形態的な特徴とウィッグ装着時の頭部状態抽出)を引き続き実施する。また今年度は医工学的手法と分子学的手法により頭皮の炎症レベルの策定を実施計画としている。脱毛頭皮の構造について脱毛時の頭皮構造と栄養血管について超音波画像装置を用いて変化を可視化の可能性を評価した。

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  • 乳児股関節の超音波画像診断支援システムの開発

    2016.4 - 2019.3

    System name:科学研究費助成事業

    Research category:基盤研究(C)

    Awarding organization:日本学術振興会

    李鎔範

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    Authorship:Principal investigator  Grant type:Competitive

    将来的に生活の質を著しく低下させる可能性のある乳児股関節異常の早期発見および早期治療のために,乳児股関節検診で撮影される超音波画像を定量的にコンピュータで解析しその解析結果を診断の参考情報として活用する乳児股関節検診用コンピュータ支援診断(computer-aided diagnosis:CAD)システムの開発を目的とする.

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  • 感覚間相互作用を応用した快適重視型マンモグラフィケアプログラムの開発

    2014.4 - 2017.3

    System name:科学研究費助成事業

    Research category:基盤研究(C)

    Awarding organization:日本学術振興会

    内山美枝子, 李鎔範

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    Grant type:Competitive

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  • Development of a care program to decrease physical and psychological burden for mammography examinees

    Grant number:23593285

    2011.4 - 2014.3

    System name:Grants-in-Aid for Scientific Research

    Research category:Grant-in-Aid for Scientific Research (C)

    Awarding organization:Japan Society for the Promotion of Science

    LEE Yongbum, UCHIYAMA Mieko

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    Authorship:Principal investigator  Grant type:Competitive

    The purpose of this study is to develop a care program for decreasing the physical and psychological burden on examinees during mammography. In this study, we conducted some experiments with regards to the measurement of muscle activity of the mammography examinees, or the measurement of autonomic nervous activity of the mammography examinees, or the effect of a humorous video on reduction of examinee's burden during mammography. As results, quantification method of physical and psychological burden for the mammography examinees was developed. Experimental results also showed that an intervention or stimulus from the outside had the potential to decrease the burden of the mammography examinees.

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  • Establishment of an integrated evaluation metric for medical image quality using mutual information

    Grant number:23602004

    2011.4 - 2014.3

    System name:Grants-in-Aid for Scientific Research

    Research category:Grant-in-Aid for Scientific Research (C)

    Awarding organization:Japan Society for the Promotion of Science

    TSAI Du-Yih, LEE Yongbum

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    Grant type:Competitive

    In this study, we proposed an information-theoretic method for quantifying overall image quality in terms of mutual information (MI). The MI is used to express the amount of information that an output image contains about an input object. We investigated the utility of this method by applying it to evaluate images obtained from various modalities. We also compared evaluation results in terms of MI against that in terms of the detective quantum efficiency, signal-to-noise ratio, standard deviations, which are conventionally used for characterizing the efficiency performance of imaging systems. Our results demonstrated that the proposed method is simple to implement, and has potential usefulness for evaluation of overall image quality. In conclusion, we reveal that the approach using mutual-information-base image quality metric is well established.

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  • Development of a computer aided system for detecting early CT signs of hyperacute stroke

    Grant number:19591402

    2007.4 - 2010.3

    System name:Grants-in-Aid for Scientific Research

    Research category:Grant-in-Aid for Scientific Research(C)

    Awarding organization:Japan Society for the Promotion of Science

    Du-Yih Tsai, Motomasa Kimura, Yongbum Lee

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    Grant type:Competitive

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Teaching Experience

  • 医療情報システム概論

    2024
    Institution name:新潟大学

  • 医療画像処理工学

    2024
    Institution name:新潟大学

  • 保健学総合

    2023
    Institution name:新潟大学

  • 医療画像工学II

    2023
    Institution name:新潟大学

  • 医療画像工学I

    2023
    Institution name:新潟大学

  • 保健理工学概論

    2022
    Institution name:新潟大学

  • 放射線撮影技術学演習

    2022
    -
    2023
    Institution name:新潟大学

  • 放射線科学セミナー

    2021
    Institution name:新潟大学

  • 保健学特定研究(放射線技術科学)

    2021
    Institution name:新潟大学

  • 医用画像情報学特講演習

    2021
    Institution name:新潟大学

  • 医療情報学

    2021
    Institution name:新潟大学

  • 医用画像情報学演習

    2021
    Institution name:新潟大学

  • 卒業研究

    2021
    Institution name:新潟大学

  • 医用情報システム概論

    2021
    -
    2023
    Institution name:新潟大学

  • 医用画像工学

    2021
    -
    2023
    Institution name:新潟大学

  • 医療と放射線

    2021
    Institution name:新潟大学

  • 医用画像処理工学演習

    2021
    Institution name:新潟大学

  • 放射線撮影技術学実習

    2020
    Institution name:新潟大学

  • 生命と生活の健康科学

    2018
    Institution name:新潟大学

  • 放射線写真学

    2017
    Institution name:新潟大学

  • 医療と放射線

    2017
    -
    2021
    Institution name:新潟大学

  • 医療英語(放射)

    2016
    Institution name:新潟大学

  • 放射線科学セミナー

    2016
    -
    2018
    Institution name:新潟大学

  • 卒業研究

    2016
    -
    2018
    Institution name:新潟大学

  • リサーチ・メソッズ・アドバンスト

    2015
    Institution name:新潟大学

  • リサーチ・メソッズ・ベーシック

    2015
    -
    2018
    Institution name:新潟大学

  • 医用画像情報学特講

    2014
    Institution name:新潟大学

  • 保健学特別研究(放射線技術科学)

    2014
    Institution name:新潟大学

  • 保健学特定研究(放射線技術科学)

    2014
    -
    2018
    Institution name:新潟大学

  • 情報科学概論演習

    2014
    -
    2016
    Institution name:新潟大学

  • 医用画像情報学特講演習

    2013
    -
    2018
    Institution name:新潟大学

  • 環日本海医療概論

    2013
    -
    2018
    Institution name:新潟大学

  • 超音波技術学

    2011
    -
    2018
    Institution name:新潟大学

  • スタディスキルズ (放射)

    2010
    -
    2023
    Institution name:新潟大学

  • 放射線診療技術科学特別研究

    2008
    -
    2013
    Institution name:新潟大学

  • 医用画像情報学特論

    2007
    Institution name:新潟大学

  • 医用情報システム概論

    2007
    -
    2023
    Institution name:新潟大学

  • 医用画像工学

    2007
    -
    2023
    Institution name:新潟大学

  • 医用画像処理工学

    2007
    -
    2023
    Institution name:新潟大学

  • 医用画像工学実験

    2007
    -
    2021
    Institution name:新潟大学

  • 医用画像処理工学演習

    2007
    -
    2021
    Institution name:新潟大学

  • 医用画像情報学演習

    2007
    -
    2018
    Institution name:新潟大学

  • 医療情報学

    2007
    -
    2018
    Institution name:新潟大学

  • 画像情報学

    2007
    Institution name:新潟大学

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