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| Automatic Detection of the Masses in the Mammograms Using Characteristic Modeling and Neural Networks |
| Xu Wei-dong①; Liu Wei①; Li Li-hua①; Xia Shun-ren②;Ma Li①;Shao Guo-liang③;Zhang Juan③ |
| ①Institute for Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China; ②Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;③Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China |
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Abstract Mammography is a conventional early detection method for breast cancer. A novel Computer-Aided Diagnosis (CAD) method for the masses is proposed in this paper. Two characteristic models are built up to represent the masses with various backgrounds, and iterative thresholding is carried out to detect the masses in the fatty tissue; however, black-hole detection of wavelet-domain is applied to label the masses in the dense tissue. Filling dilation based on ANFIS controller, Canny detector and the energy field constraint is used to segment the suspicious masses, and MLP-based classifier is applied to suppress the false positives. The experiments validate that the proposed algorithm gets high detection precision, as well as low false positive rate.
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Received: 30 May 2008
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2009, Vol. 31 
