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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.

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.

In general, several factors are used for risk estimation in breast cancer detection and early prevention, and one of the important factors in risk of breast cancer is breast density. The mammography is important and effective adjunct in diagnosing the breast cancer. The radiologists would analyze visually the breast density with the BI-RADS lexicon on mammograms. However, this usually causes a large inter-observer variability among the different experienced radiologists. In this paper, we individually adopt three methods, including pixel-based, region-based, and physics-based, to analyze the breast density on mammograms, and the results can offer radiologists a second quantification reading for predicting the risk of breast cancer. The three methods are tested on 208 digital and conventional film mammograms which are scanned from both breasts of 104 patients respectively. The experimental results show that the accuracy of the proposed region-based method, which is more consistent with the radiologists' viewpoint, is 88% more than other two conventional methods.

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.

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.

The breast density information is one of important factors for estimating the risk in breast cancer detection and early prevention. In this paper, we present two methods, including threshold-based and proportion-based, to automatically analyze the breast density using whole breast ultrasound. The two algorithms are experimented with 32 cases which are scanned from 32 patients using the US machine SSD-5500 with a recent developed scanner ASU-1004 (Aloka, Japan). The experimental results are graded from 4 (extremely dense tissue) to 1 (almost entirely fat), and respectively compared with the majority grades of three radiologists. The accuracy of the threshold-based and proportion-based strategies is 88% and 84% respectively.

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.

Early detection through screening is the best defense against morbidity and mortality from breast cancers. Mammography is the most used screening tool for detecting early breast cancer because it can easily obtain the view of whole breast. However, because the ultrasound images are cross-sectional images, not projection images like mammography, and the ultrasound probe does not fully cover the breast width, it is not a convenient screening tool when adjunct with screening mammography. The physician needs a lot of examination time to perform the breast screening. Recently, some whole breast ultrasound scanning machines are developed. The examination could be performed by an experienced technician. Because the probe width still does not fully cover the breast width, several scanning passes are required to obtain the whole breast image. The physician still cannot have a full view of breast. In this paper, an image stitching technique is proposed to stitch multi-pass images into a full-view image. The produced full-view image can reveal the breast anatomy and assists physicians to reduce extra manual adjustment.

Mass screening of breast cancer utilizing mammography (MMG) has been widely carried out. However, MMG might not be able to depict small impalpable masses in dense breast tissue clearly. We have developed a computer-aided detection (CAD) scheme in whole breast ultrasound (US) system for mass screening which has been developed by ALOKA CO., LTD., Japan. Our CAD scheme and image processing techniques have the following three benefits. Indication of mass candidates by our CAD scheme. Visualization of breast US images in two views of B-planes (CC View and ML View) and C-plane. Comparison of left and right breast images as in MMG. The performance of the CAD scheme in detecting malignant masses on an initial study has a true positive fraction of 0.91 (10/11) at a 0.69 (633/924) false positive per image. Although mass screening utilizing US was not appropriate because images acquired by conventional hand probe were poor in reproduction, the problem could be solved in our system.

To improve the ability of technicians and physicians to find and diagnose breast lesions in breast ultrasound screening.Seminars were organized for technicians and physicians engaged in breast ultrasound screening, and tests were carried out to evaluate the usefulness of the seminars. Each seminar lasted 2 days and comprised lectures and group activities. Pretests and post-tests conducted before and after each seminar, respectively, consisted of 100 questions: 50 about animated images, and 50 about static images. The tests required the participant to find lesions in animated images and estimate the probability of malignancy from static images.In the animated image tests, sensitivity was greater after the seminar, although specificity did not change significantly. In the static image tests, sensitivity increased, and a significant increase was also observed in the receiver operating curve analysis for degree of certainty in diagnosing cancer.The seminars improved the participants' ability to find and diagnose breast lesions during ultrasound screening.

WCN 2013 No: 936 Topic: 2—Movement Disorders Clinical correlates of serum insulin-like growth factor 1 in patients with Parkinson's disease, multiple system atrophy and progressive supranuclear palsy A. Numao, K. Suzuki, M. Miyamoto, T. Miyamoto, K. Hirata. Department of Neurology, Dokkyo Medical University, Mibu, Japan; Department of Neurology, DokkyoMedical University Koshigaya Hospital