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17 March 2008 Rib detection for whole breast ultrasound image
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Abstract
Recently, the whole breast ultrasound (US) is a new advanced screening technique for detecting breast abnormalities. Because a lot of images are acquired for a case, the computer-aided system is needed to help the physicians to reduce the diagnosis time. In the automatic whole breast US, the ribs are the pivotal landmark just like the pectoral muscle in the mammography. In this paper, we develop an automatic rib detection method for the whole breast ultrasound. The ribs could be helpful to define the screening area of a CAD system to reduce the tumor detection time and could be used to register different passes for a case. In the proposed rib detection system, the whole breast images are subsampled at first in order to reduce the computation of rib detection without reducing the detection performance. Due to the shadowing is occurred under the rib in the whole breast ultrasound images and is the sheet-like structure, the Hessian analysis and sheetness function are adopted to enhance the sheet-like structure. Then, the orientation thresholding is adopted to segment the sheet-like structures. In order to remove the non-rib components in the segmented sheet-like structures, some features of ribs in whole breast ultrasound are used. Thus, the connected component labeling is applied and then some characteristics such as orientation, length and radius are calculated. Finally, some criteria are applied to remove non-rib components. In our experiments, there are 65 ribs in 15 test cases and the 62 ribs have been detected by the proposed system with the detection ratio 95.38%. The ratio of position difference under 5 mm is 87.10 % and the ratio of length difference under 10 mm is 85.48 %. The results show that the proposed system almost could detect the ribs in the breast US images and has a good accuracy.
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Ruey-Feng Chang, Yi-Wei Shen, Jiayu Chen, Yi-Hong Chou, and Chiun-Sheng Huang "Rib detection for whole breast ultrasound image", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691525 (17 March 2008); https://doi.org/10.1117/12.769602
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