The word "Locomotive syndrome" has been proposed to describe the state of requiring care by musculoskeletal disorders and its high-risk condition. Reduction of the knee extension strength is cited as one of the risk factors, and the accurate measurement of the strength is needed for the evaluation. The measurement of knee extension strength using a dynamometer is one of the most direct and quantitative methods. This study aims to develop a system for measuring the knee extension strength using the ultrasound images of the rectus femoris muscles obtained with non-invasive ultrasonic diagnostic equipment. First, we extract the muscle area from the ultrasound images and determine the image features, such as the thickness of the muscle. We combine these features and physical features, such as the patient’s height, and build a regression model of the knee extension strength from training data. We have developed a system for estimating the knee extension strength by applying the regression model to the features obtained from test data. Using the test data of 168 cases, correlation coefficient value between the measured values and estimated values was 0.82. This result suggests that this system can estimate knee extension strength with high accuracy.
Diagnostic imaging on FDG-PET scans was often used to evaluate chemotherapy results of cancer patients. Radiologists compare the changes of lesions' activities between previous and current examinations for the evaluation. The purpose of this study was to develop a new computer-aided detection (CAD) system with temporal subtraction technique for FDGPET scans and to show the fundamental usefulness based on an observer performance study. Z-score mapping based on statistical image analysis was newly applied to the temporal subtraction technique. The subtraction images can be obtained based on the anatomical standardization results because all of the patients' scans were deformed into standard body shape. An observer study was performed without and with computer outputs to evaluate the usefulness of the scheme by ROC (receiver operating characteristics) analysis. Readers responded as confidence levels on a continuous scale from absolutely no change to definitely change between two examinations. The recognition performance of the computer outputs for the 43 pairs was 96% sensitivity with 31.1 false-positive marks per scan. The average of area-under-the-ROC-curve (AUC) from 4 readers in the observer performance study was increased from 0.85 without computer outputs to 0.90 with computer outputs (p=0.0389, DBM-MRMC). The average of interpretation time was slightly decreased from 42.11 to 40.04 seconds per case (p=0.625, Wilcoxon test). We concluded that the CAD system for torso FDG-PET scans with temporal subtraction technique might improve the diagnostic accuracy of radiologist in cancer therapy evaluation.
ROC studies require complex procedures to select cases from many data samples, and to set confidence levels in
each selected case to generate ROC curves. In some observer performance studies, researchers have to develop software
with specific graphical user interface (GUI) to obtain confidence levels from readers. Because ROC studies could be
designed for various clinical situations, it is difficult task for preparing software corresponding to every ROC studies. In
this work, we have developed software for recording confidence levels during observer studies on tiny personal handheld
devices such as iPhone, iPod touch, and iPad. To confirm the functions of our software, three radiologists performed
observer studies to detect lung nodules by using public database of chest radiograms published by Japan Society of
Radiological Technology. The output in text format conformed to the format for the famous ROC kit from the University
of Chicago. Times required for the reading each case was recorded very precisely.
Ultrasonography is one of the most important methods for breast cancer screening in Japan. Several mechanical
whole breast ultrasound (US) scanners have been developed for mass screening. We have reported a computer-aided
detection (CAD) scheme for the detection of masses in whole breast US images. In this study, the method
of detecting mass candidates and the method of reducing false positives (FPs) were improved in order to enhance
the performance of this scheme. A 3D difference (3DD) filter was newly developed to extract low-intensity regions.
The 3DD filter is defined as the difference of pixel values between the current pixel value and the mean pixel value
of 17 neighboring pixels. Low-intensity regions were efficiently extracted by use of 3DD filter values, and FPs were
reduced using a FP reduction method employing the rule-based technique and quadratic discriminant analysis
with the filter values. The performance of our previous and improved CAD schemes indicated a sensitivity of
80.0% with 16.8 FPs and 9.5 FPs per breast, respectively. The FPs of the improved scheme were reduced by
44% as compared to the previous scheme. The 3DD filter was useful for the detection of masses in whole breast
US images.
The comparison of left and right mammograms is a common technique used by radiologists for the detection and
diagnosis of masses. In mammography, computer-aided detection (CAD) schemes using bilateral subtraction
technique have been reported. However, in breast ultrasonography, there are no reports on CAD schemes using
comparison of left and right breasts. In this study, we propose a scheme of false positive reduction based on
bilateral subtraction technique in whole breast ultrasound images. Mass candidate regions are detected by using
the information of edge directions. Bilateral breast images are registered with reference to the nipple positions
and skin lines. A false positive region is detected based on a comparison of the average gray values of a mass
candidate region and a region with the same position and same size as the candidate region in the contralateral
breast. In evaluating the effectiveness of the false positive reduction method, three normal and three abnormal
bilateral pairs of whole breast images were employed. These abnormal breasts included six masses larger than
5 mm in diameter. The sensitivity was 83% (5/6) with 13.8 (165/12) false positives per breast before applying
the proposed reduction method. By applying the method, false positives were reduced to 4.5 (54/12) per breast
without removing a true positive region. This preliminary study indicates that the bilateral subtraction technique
is effective for improving the performance of a CAD scheme in whole breast ultrasound images.
Breast cancer mass screening is widely performed by mammography but in some population with dense
breast, ultrasonography is much effective for cancer detection. For this purpose it is necessary to
develop special ultrasonic equipment and the system for breast mass screening. It is important to
design scanner, image recorder, viewer with CAD (Computer-assisted detection) as a system. Authors
developed automatic scanner which scans unilateral breast within 30 seconds. An electric linear probe
visualizes width of 6cm, the probe moves 3 paths for unilateral breast. Ultrasonic images are recorded
as movie files. These files are treated by microcomputer as volume data. Doctors can diagnose by
digital rapid viewing with 3D function. It is possible to show unilateral or bilateral images on a screen.
The viewer contains reporting function as well. This system is considered enough capability to
perform ultrasonic breast cancer mass screening.
We have investigated Computer-aided detection (CAD) system for breast masses on screening ultrasound (US) images. A lot of methods of Computer-aided detection and diagnosis system on US images have been developed by many researchers in the world. However, some methods require substantial computation time in analysing a US image, and some systems also need a radiologist to indicate the masses in advance. In this paper, we proposed fast automatic detection system which utilizes edge information in detecting masses. Our method consists of the following steps: (1) noise reduction and image normalization, (2) decision of the region of interest (ROI) using vertical edges detected by the canny edge detector, (3) segmentation of ROI using watershed algorithm, and (4) reduction of false positives. This study employs 11 whole breast cases with a total of 924 images. All the cases have been diagnosed by a radiologist prior to the study. This database have 11 malignant masses. These malignant masses have heterogeneous internal echo, a low or equal echo-level, and a deficient or disappearance posterior echo. Using the proposed method, the sensitivity in detecting malignant masses is 90.9% (10/11) and the number of false positives per image is 0.69 (633/924). It is concluded that our method is effective for detecting breast masses on US images.
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