Multidirectional Gabor Filter-Based Approach for Pectoral Muscle Boundary Detection

Abstract

Accurate pectoral muscle boundary (PMB) detection is a crucial stage in any computer-aided detection (CAD) system, used for automatic detection of various possible signs of malignancy in a mammographic image. The presence of high-density glandular tissues overlapping PMB, pectoral muscle of small size and low density, presence of pectoralis minors, etc., poses a significant challenge for researchers working on automatic PMB detection problem. In mammograms with pectoral muscle superimposed by high-density glandular tissues, especially near the lower portion of the pectoral muscle, PMB forms fuzzy textural edges with surrounding mammary tissues regions. The performance of any intensity-based approach in detecting such fuzzy textural edges is poor. Here, we present a multidirectional Gabor filter (MDGF)-based approach for PMB detection. A set of three high-frequency bandpass Gabor filters is designed, which covers all the possible orientations of PMB present in a preprocessed mediolateral oblique (MLO) view mammogram. These filters are used to extract high- and mid-frequency range edge information corresponding to strong as well as weak fuzzy textural edges of PMB. The information obtained from magnitude and phase response along with a boundary search and merge algorithm (designed to connect broken PMB edges) is used to detect complete PMB with high accuracy. With PMB detection accuracy of 95.28% for MIAS, 96.50% for CBIS-DDSM, and 96.95% for INbreast database mammograms, the proposed method outperforms many state-of-the-art methods, thus show its suitability for a CAD system.