A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis

Authors:
Chen-Chung Liu;Chung-Yen Tsai;Jui Liu;Chun-Yuan Yu;Shyr-Shen Yu
Affiliations:
Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan;Department of Computer Science and Engineering, National Chung-Hsing University, Taichung 400, Taiwan;Department of General Medicine, Royal North Shore Hospital, Sydney, Australia;Department of Computer Science and Engineering, National Chung-Hsing University, Taichung 400, Taiwan;Department of Computer Science and Engineering, National Chung-Hsing University, Taichung 400, Taiwan
Venue:
Computers & Mathematics with Applications
Year:
2012

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Abstract

One of the issues when interpreting a mammogram is that the density of a pectoral muscle region is similar to the tumor cells. The appearance of pectoral muscle on medio-lateral oblique (MLO) views of mammograms will increase the false positives in computer aided detection (CAD) of breast cancer. For this reason, pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm is based on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphological processing to find a rough border of the pectoral muscle. The multiple regression analysis (MRA) is then employed on this rough border to obtain an accurate segmentation of the pectoral muscle. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the pectoral muscle extracted by the presented algorithm approximately follows that extracted by an expert radiologist.