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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