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

DOI： 10.5100/jje.61.51

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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

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

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

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

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

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

DOI： 10.11318/mii.41.15

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

DOI： 10.11318/mii.40.56

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

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

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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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

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

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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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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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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

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

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

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

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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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Other Link： https://link.springer.com/article/10.1007/s11548-022-02694-0/fulltext.html

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

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.

DOI： 10.1038/s41598-022-20748-w

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Other Link： https://www.nature.com/articles/s41598-022-20748-w

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

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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Other Link： https://link.springer.com/article/10.1007/s12194-022-00675-1/fulltext.html

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPIE  

DOI： 10.1117/12.2624139

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

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.

DOI： 10.1088/2057-1976/ac7ada

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Other Link： https://iopscience.iop.org/article/10.1088/2057-1976/ac7ada/pdf

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

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.

DOI： 10.1186/s12891-022-05565-6

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Other Link： https://link.springer.com/article/10.1186/s12891-022-05565-6/fulltext.html

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

&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;

DOI： 10.21014/acta_imeko.v11i2.1168

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

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.

DOI： 10.1007/s12194-022-00657-3

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

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

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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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Other Link： https://search.jamas.or.jp/default/link?pub_year=2022&ichushi_jid=J01192&link_issn=&doc_id=20220126400003&doc_link_id=1390853741275705216&url=https%3A%2F%2Fcir.nii.ac.jp%2Fcrid%2F1390853741275705216&type=CiNii&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00003_3.gif

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

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Elsevier BV  

DOI： 10.1016/j.measen.2021.100089

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Elsevier BV  

DOI： 10.1016/j.measen.2021.100127

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Publishing type：Research paper (international conference proceedings)   Publisher：SPIE  

DOI： 10.1117/12.2581424

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

<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.

</sec>

DOI： 10.1371/journal.pone.0245060

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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.

DOI： 10.1007/s12194-020-00586-z

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Other Link： http://link.springer.com/article/10.1007/s12194-020-00586-z/fulltext.html

Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)  

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

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

DOI： 10.1007/s11604-020-00986-6

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Other Link： https://link.springer.com/article/10.1007/s11604-020-00986-6/fulltext.html

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DOI： 10.1117/12.2562180

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DOI： 10.1117/12.2555276

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DOI： 10.1007/s11604-019-00894-4

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：MEDICAL IMAGING AND INFORMATION SCIENCES  

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⁻¹and 4.6 mm⁻¹, 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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Other Link： https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17K17738/

Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：MEDICAL IMAGING AND INFORMATION SCIENCES  

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.

DOI： 10.11318/mii.36.83

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

DOI： 10.11318/mii.36.64

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DOI： 10.1117/12.2507351

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DOI： 10.11409/mit.36.217

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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.

DOI： 10.6009/jjrt.2018_JSRT_74.11.1286

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Other Link： http://id.ndl.go.jp/bib/029359646

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

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.

DOI： 10.6009/jjrt.2018_JSRT_74.10.1138

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Scientific Publishers  

DOI： 10.1166/jmihi.2018.2398

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.14923/transinfj.2017IGL0001

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DOI： 10.6009/jjrt.2017_JSRT_73.11.1125

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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.

DOI： 10.11318/mii.34.92

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Other Link： https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16K09012/

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.4236/jbise.2017.103009

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DOI： 10.11409/mit.36.32

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：MEDICAL IMAGING AND INFORMATION SCIENCES  

<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>

DOI： 10.11318/mii.33.63

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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.

DOI： 10.11318/mii.33.16

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Tokyo  

DOI： 10.1007/s12194-015-0335-0

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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.

DOI： 10.6009/jjrt.2015_JSRT_71.11.1063

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DOI： 10.4236/jbise.2015.810067

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DOI： 10.4236/health.2015.76078.

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DOI： 10.4236/health.2015.72026

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DOI： 10.4236/health.2015.71004

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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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Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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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Other Link： http://search.jamas.or.jp/link/ui/2014238123

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer-Verlag Tokyo  

DOI： 10.1007/s12194-013-0234-1

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

DOI： 10.1007/978-3-319-07887-8_71

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

J-GLOBAL

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DOI： 10.4236/health.2013.58181

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DOI： 10.1007/s10278-012-9555-6

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DOI： 10.1007/978-3-642-29305-4_229

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Other Link： http://search.jamas.or.jp/link/ui/2013307664

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/EMBC.2013.6610272

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DOI： 10.1007/978-3-642-29305-4_233

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DOI： 10.1620/tjem.228.53

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DOI： 10.1007/s12194-012-0145-6

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

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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DOI： 10.1007/s12194-011-0143-0

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

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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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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.

DOI： 10.6009/jjrt.67.679

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：MEDICAL IMAGING AND INFORMATION SCIENCES  

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.

DOI： 10.11318/mii.28.51

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Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society of Medical Imaging Technology  

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.

DOI： 10.11409/mit.29.17

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DOI： 10.1109/BMEI.2011.6098601

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DOI： 10.1117/12.877740

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DOI： 10.1117/12.877740

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DOI： 10.1118/1.3516238

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society of Medical Imaging Technology  

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.

DOI： 10.11409/mit.28.371

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DOI： 10.1097/RCT.0b013e3181e66473

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Language：Japanese   Publisher：日本医用画像工学会  

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

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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DOI： 10.1016/j.acra.2009.07.011

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DOI： 10.1088/1742-6596/238/1/012055

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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.

DOI： 10.11318/mii.27.29

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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.

DOI： 10.6009/jjrt.65.1618

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DOI： 10.6009/jjrt.65.1313

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DOI： 10.1117/1.3206967

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Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)   Publisher：Niigata University  

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Other Link： http://search.jamas.or.jp/link/ui/2009151083

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DOI： 10.1117/12.811082

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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.

DOI： 10.11318/mii.25.54

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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).

DOI： 10.11318/mii.25.48

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DOI： 10.1007/s10278-007-9044-5

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Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)   Publisher：Niigata University  

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Other Link： http://search.jamas.or.jp/link/ui/2008264517

Language：English   Publishing type：Research paper (bulletin of university, research institution)   Publisher：Niigata University  

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Other Link： http://search.jamas.or.jp/link/ui/2008264516

Publishing type：Research paper (scientific journal)  

DOI： 10.6009/jjrt.64.881

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DOI： 10.1080/02841850802126570

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DOI： 10.1117/12.768324

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DOI： 10.1117/12.768324

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DOI： 10.1007/s11548-007-0123-3

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology (JSRT)  

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]

DOI： 10.6009/jjrt.63.835

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DOI： 10.1007/s11604-007-0129-3

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DOI： 10.6009/jjrt.63.341

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Other Link： http://hdl.handle.net/10191/8168

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology (JSRT)  

DOI： 10.6009/jjrt.62.447

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Other Link： http://hdl.handle.net/10191/4863

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1117/12.654242

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DOI： 10.1117/12.652908

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DOI： 10.1117/12.654242

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Verlag  

DOI： 10.1007/s11548-006-0031-y

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DOI： 10.1117/12.652908

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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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Other Link： http://hdl.handle.net/10191/4862

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11409/mit.23.239

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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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Other Link： http://hdl.handle.net/10191/4861

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

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Language：Japanese   Publisher：公益社団法人 日本放射線技術学会  

DOI： 10.6009/jjrt.KJ00004028181

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DOI： 10.1117/1.1786607

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：(公社)日本医学物理学会  

DOI： 10.11323/jjmp2000.24.3_115

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Other Link： https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2004&ichushi_jid=J03418&link_issn=&doc_id=20041201260002&doc_link_id=10014056519&url=https%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10014056519&type=CiNii&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00003_1.gif

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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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Other Link： http://search.jamas.or.jp/link/ui/2005025516

Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：MEDICAL IMAGING AND INFORMATION SCIENCES  

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.

DOI： 10.11318/mii.21.122

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DOI： 10.1117/12.536162

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DOI： 10.1117/12.536274

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Language：Japanese   Publisher：公益社団法人 日本放射線技術学会  

DOI： 10.6009/jjrt.KJ00003174512

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：新潟大学医学部保健学科  

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Other Link： http://search.jamas.or.jp/link/ui/2004194207

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11239/jsmbe2002.41.105

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DOI： 10.1109/ICNNSP.2003.1281059

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Radiological Technology  

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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Other Link： http://search.jamas.or.jp/link/ui/2002207001

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

DOI： 10.11239/jsmbe1963.40.86

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DOI： 10.1002/ecjc.10025

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DOI： 10.1109/ICOSP.2002.1181158

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Other Link： http://hdl.handle.net/10191/6555

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11318/mii1984.18.135

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DOI： 10.1109/42.932744

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DOI： 10.1118/1.1382823

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Language：Japanese   Publisher：Japanese Society of Radiological Technology  

DOI： 10.6009/jjrt.KJ00003111315

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DOI： 10.11318/mii1984.17.118

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DOI： 10.1109/ICIAP.1999.797688

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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

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Medical Imaging and Information Sciences  

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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Total pages：153   Language：Japanese

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Total pages：835   Language：Japanese

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Total pages：1618   Language：Japanese

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Language：Japanese   Publisher：ニューサイエンス社  

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Other Link： https://search.jamas.or.jp/link/ui/2019264912

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Objective evaluation, e.g. using MTF or NPS, of X-ray imaging system may not correctly correspond to subject evaluation, e.g. using ROC. Moreover, it is difficult to measure MTF or NPS from clinical images. Therefore, we introduced MTF fractal dimension to X-ray imaging system. The MTF fractal dimension includes frequency properties of human eyes in addition to quantitative complexity. In this study, we conducted an observer performance study using chest X-ray images with subtle simulated nodules, and also measured MTF fractal dimension from the same images. The measurement results of MTF fractal dimension tended to be similar to the results of observer performance study. Therefore, we consider that MTF fractal dimension may become an index of image quality in medical X-ray imaging system.

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Other Link： http://hdl.handle.net/20.500.12099/53321

Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In this study, we proposed an automated recognition method for blurred radiographs. The method involved edge detection using sobel filter, automatically determining region of interest (ROI), feature calculation, and classification using 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 classification accuracies of images with blurs and images without blurs were 100% for both phantom and clinical images.

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Language：Japanese   Publisher：新潟大学教育・学生支援機構  

平成23年度新潟大学組織的教育プログラム(新潟大学GP)医学部保健学科取組「学生と教員が学び鍛えあう保健学教育」は,医療を支える使命感と能力をもち,チーム医療に対応できる人材の育成をめざす2つの教育プログラムの実質化を進めた.保健学ヘの動機づけを図るプログラム「My confident lecture」では,新規科目「保健学総合」を初年次学生向けに開講し,学生参加型授業などの新しいタイプの授業の試みを行った.「保健学総合」を実践的に展開するプログラム「Our project study」では,少人数型の演習科目「保健学総合演習」の平成24年度開講をめざして模擬授業を試みた.それらの活動内容について,取組の課題とともに紹介する.

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Language：Japanese   Publisher：日本医用画像工学会  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：The Japanese Society of Medical Imaging Technology  

Identification of loss of the gray-white matter interface (GWMI) is important for the diagnosis of acute stroke in unenhanced CT. However, the identification is difficult due to the intrinsic presence of quantum noise on CT images. As a result, low sensitivity of detection of loss of the GWMI becomes a major issue in unenhanced CT. To cope with this issue, we have previously developed an adaptive partial median filter (APMF). In order to validate the usefulness of the APMF, we have evaluated the performance of the proposed filter using simulated images and clinical images. This report first describes the outline of the APMF and its performance. Next, the practically clinical usefulness of the APMF is presented based on experimental results.

DOI： 10.11409/mit.30.231

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Other Link： http://search.jamas.or.jp/link/ui/2013203999

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：日本放射線技術学会画像分科会  

DOI： 10.18973/cigjsrt.35.2_66

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：日本放射線技術学会画像分科会  

DOI： 10.18973/cigjsrt.35.1_82

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

A novel application of computer-aided diagnosis (CAD), namely autonomous CAD, was proposed in this study. Autonomous CAD can obtain medical images in front of it by computer vision (CV), and detect abnormalities from the obtained image, and output the detection result to the position space. Some fundamental techniques are required to materialize the autonomous CAD, e.g. detection algorithm of abnormalities in radiographs, and recognition algorithm of moving radiographs. We developed computerized detection schemes for lung nodules in X-ray images. Sixty chest radiographs and twenty CT scans were used to evaluate the detection schemes. These images were displayed on a monitor, and taken by an optical camera as the CV images. The detection schemes were applied to the obtained CV images. Our results showed that there was no difference in detection performances even if the CV image was used as input of the detection scheme. We also developed a scheme to recognize radiographs in CV moving images. The scheme was applied to ten moving images with scenes including radiographs. It was able to nearly correctly recognize moving radiographs. The developed schemes would become fundamental techniques in future autonomous CAD system.

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：The Japanese Society of Medical Imaging Technology  

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.

DOI： 10.11409/mit.29.17

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)   Publisher：Japanese Society of Radiological Technology  

DOI： 10.6009/jjrt.66.1614

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Other Link： https://jlc.jst.go.jp/DN/JALC/00362227343?from=CiNii

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：日本放射線技術学会画像分科会  

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Abstract is not available. (Article in Japanese)

DOI： 10.6009/jjrt.64.1000

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Other Link： https://jlc.jst.go.jp/DN/JALC/00317643914?from=CiNii

Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Other Link： http://search.jamas.or.jp/link/ui/2008298848

Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：日本放射線技術学会画像分科会  

DOI： 10.18973/cigjsrt.30.2_68

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：日本放射線技術学会画像分科会  

DOI： 10.18973/cigjsrt.30.1_79

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Language：English   Publisher：Springer New York  

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Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：Japanese   Publisher：公益社団法人日本放射線技術学会  

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Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

We have developed a new recognition approach using 2nd order autocorrelation and multi-regression analysis to detect a small (<7mm in diameter) lung nodules in chest 3D CT images. By combining our previous detection method of the template matching based on genetic algorithm, the detection performance was 94% true-positive rate at 2.05 false-positive marks per case using leave-one-out study.

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Language：Japanese   Publisher：Japanese Society for Medical and Biological Engineering  

The purpose of this study is to develop a computerized scheme that allows discrimination between benign and malignant clustered microcalcifications, thus aiding radiologists to better interpret mammograms. The fuzzy-logic based genetic algorithm (GA-Fuzzy) that we propose is applied for the classification of mammographic microcalcifications. As a preprocess, microcalcification regions are extracted from images using mathematical morphology and other means. Then, four feature values including the number, area, circularity and minimum distance of microcalcifications are calculated for classification using GA-Fuzzy. In the GA-Fuzzy process, Gaussian-distributed membership functions are determined from the means and standard deviations of the feature values calculated from training images. Subsequently, the genetic algorithm optimizes the membership functions, and training is completed. Next, the feature values calculated from unknown images are input into GA-Fuzzy system to classify the image. Our scheme was tested utilizing the database of the Mammographic Image Analysis Society (MIAS), which contains 13 benign and 12 malignant microcalcification cases. Of the images, there are ten of each benign and malignant cases respectively used for training. The remaining five cases were used for classification as unknown images. Various combinations of sets are employed to obtain the results. The average accuracy of the GA-Fuzzy technique was approximately 81% (sensitivity, 85%; specificity, 77%). These results show that our scheme can be regarded as a useful technique to classify microcalcification cases.

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Other Link： http://search.jamas.or.jp/link/ui/2004017840

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：医用画像情報学会  

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Language：English   Publishing type：Research paper, summary (international conference)  

Web of Science

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Language：Japanese  

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Other Link： http://search.jamas.or.jp/link/ui/2003037691

Language：Japanese  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：日本放射線技術学会画像分科会  

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Language：Japanese  

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Language：Japanese  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

We have proposed a technique, by which a template matching of gray-level images can be performed accurately and fastly in a large range of searching regions, and in which a genetic algorithm(GA)is applied to the searching part in image recognition.The images that we have used were only 2D ones.In this paper, therefore, we applied our method to 3D images by considering an application to 3D medeical images.It was shown based on a simulation study that our method is useful in 3D space.In the application of our techique, the nodule detection on chest helical CT images is effective.

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Language：Japanese  

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Language：Japanese  

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Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

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Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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