AUTOMATED DIGITAL MAMMOGRAM SEGMENTATION FOR DETECTION OF ABNORMAL MASSES USING BINARY HOMOGENEITY ENHANCEMENT ALGORITHM
Indra Kanta Maitra, Sanjay Nag, Samir Kumar Bandyopadhyay · 2011
Abstract- Many image processing techniques have been developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breast cancer can increase the survival rate, thus making screening programmes a mandatory step for females. Radiologists have to examine a large number of images. Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD) systems that will improve detection of abnormalities in mammogram images. Extraction of the breast region by delineation of the breast contour and pectoral muscle allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. We need to perform essential pre-processing steps to suppress artifacts, enhance the breast region and then extract breast region by the process of segmentation. In this paper we present a fully automated scheme for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI). We are using medio-lateral oblique (MLO) view of mammograms. We have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA), followed by an innovative approach for edge detection (EDA). Then obtain