Automated Digital Mammogram Segmentation for Detection of Abnormal Masses Using Binary Homogeneity Enhancement Algorithm

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 the breast boundary by using our proposed Breast Boundary Detection Algorithm (BBDA). After we use our proposed Pectoral Muscle Detection Algorithm (PMDA) to suppress the pectoral muscle thus obtaining the breast ROI, we use our proposed Anatomical Segmentation of Breast ROI (ASB) algorithm to differentiate various regions within the breast. After segregating the different breast regions we use our proposed Seeded Region Growing Algorithm (SRGA) to isolate normal and abnormal regions in the breast tissue. If any abnormalities are present it gets accurately highlighted by this algorithm thus helping the radiologists to further investigate these regions. This composite method have been implemented and applied on all mammograms with abnormalities in mini-MIAS database. The algorithms proposed are fully autonomous, and are able to isolate different types of abnormalities, if present, a task very few existing mammogram segmentation algorithms can claim.