Pre-processing Algorithms on Digital Mammograms

Mammography is the best method for early mass detection. In order to limit the search for abnormalities by Computer Aided Diagnosis systems to the region of the breast without undue influence from the background of the mammogram, extraction of the breast contour and pectoral muscle is necessary. Breast contour helps to find the position of the nipple, which its position is important for mass detection in the next stages and presence of pectoral muscle in the mammogram could bias the detection procedures. So during analysis, the pectoral muscle should preferably be excluded from processing. In this paper we propose one low-pass mask for detecting breast contour and a new method for the identification of the pectoral muscle in most medio-lateral oblique mammograms based on Non-Linear Diffusion algorithm which is an edge preserving smoother. Evaluation of the breast contour and pectoral muscle detected in the mammograms were performed by the Hausdorff Distance Measure (HDM) and also the Mean of Absolute Error Distance Measure (MAEDM) based on a distance transform and image algebra between the edges identified by radiologists and by the proposed methods. Then we compare our results by other segmentation methods. Our proposed algorithms show superior results in comparison.