Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition

Abstract

Abstract. Pectoral muscle segmentation on medio-lateral oblique views of mammograms represents an important preprocessing step in many mammographic image analysis tasks. Although its location can be perceptually obvious for a human observer, the variability in shape, size, and intensities of the pectoral muscle boundary turns its automatic segmentation into a challenging problem. In this work we propose to decompose the input mammogram into its textural and structural components at different scales prior to dynamically thresholding it into several levels. The resulting segmentations are refined with an active contour model and merged together by means of a simple voting scheme to remove possible outliers. Our method performs well compared to several other state-ofthe-art techniques. An average DICE similarity coefficient of 0.91 and mean Hausdorff distance of 3.66 ± 3.23 mm. validate our approach.

Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms

“…Under an assumption that the muscle boundary is a line/curve, the line/curve detection methods propose various methods to identify or simulate the line/curve. [27][28][29][30][31][32][33][34] The main difficulty in this kind of methods is that if the pectoral muscle boundary is obscure, a line approximation or curve fitting is also difficult to perform. The classification methods regard the pectoral muscle segmentation as a dichotomous classification problem, that is, each pixel in the mammograms is classified into the target set or the non-target set.…”