Pectoral muscle detection in mammograms using local statistical features.
Mammography is a primary imaging method for breast cancer diagnosis. It is an important issue to accurately identify and separate pectoral muscles (PM) from breast tissues. Hough-transform-based methods are commonly adopted for PM detection. But their performances are susceptible when PM edges cannot be depicted by straight lines. In this study, we present a new pectoral muscle identification algorithm which utilizes statistical features of pixel responses. First, the Anderson-Darling goodness-of-fit test is used to extract a feature image by assuming non-Gaussianity for PM boundaries. Second, a global weighting scheme based on the location of PM was applied onto the feature image to suppress non-PM regions. From the weighted image, a preliminary set of pectoral muscles boundary components is detected via row-wise peak detection. An iterative procedure based on the edge continuity and orientation is used to determine the final PM boundary. Our results on a public mammogram database were assessed using four performance metrics: the false positive rate, the false negative rate, the Hausdorff distance, and the average distance. Compared to previous studies, our method demonstrates the state-of-art performance in terms of four measures.
School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, China, lliu@tju.edu.cn.
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D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods* / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods; D010369:Pectoralis Muscles / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results