Pectoral Muscle and Breast Density Segmentation Using Modified Region Growing and K-Means Clustering Algorithm

Abstract

Breast Cancer is the most habitually detected neoplasm amid women in India and it is one of the principal reasons for cancer decreases in females. In order to visualize the breast cancer, radiologists prefer to use mammogram. It consists of many artifacts, which negatively influences the detection of breast cancer. Presence of pectoral muscles makes abnormality detection a cumbersome task. The recognition of glandular tissue in mammograms is imperative in evaluating asymmetry between left and right breasts and in guesstimating the radiation risk connected with screening. Thus, the proposed technique focuses on breast part extraction, muscle part removal, enhancement of mammogram, and segmentation of mammogram images into regions conforming to different densities. The anticipated method has been verified on Mini-MIAS database mammogram images with ground truth offered by expert radiologists. The results show that the proposed technique is efficient in removing pectoral muscles and segmenting different mammographic densities.