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.
Ultrasound has been an important imaging technique for detecting breast tumors. As opposed to the conventional B-mode
image, the ultrasound elastography is a new technique for imaging the elasticity and applied to detect the stiffness
of tissues. The red region of color elastography indicates the soft tissue and the blue one indicates the hard tissue, and
the harder tissue usually is classified to malignancy. In this paper, we proposed a CAD system on elastography to
measure whether this system is effective and accurate to classify the tumor into benign and malignant. According to the
features of elasticity, the color elastography was transferred to HSV color space and extracted meaningful features from
hue images. Then the neural network was utilized in multiple features to distinguish tumors. In this experiment, there
are 180 pathology-proven cases including 113 benign and 67 malignant cases used to examine the classification. The
results of the proposed system showed an accuracy of 83.89%, a sensitivity of 85.07% and a specificity of 83.19%.
Compared with the physician's diagnosis, an accuracy of 78.33%, a sensitivity of 53.73% and a specificity of 92.92%,
the proposed CAD system had better performance. Moreover, the agreement of the proposed CAD system and the
physician's diagnosis was calculated by kappa statistics, the kappa 0.54 indicated there is a fair agreement of observers.
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.
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.
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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