Image enhancement and edge-based mass segmentation in mammogram

This paper presents a novel, edge-based segmentation method for identifying the mass contour (boundary) for a suspicious mass region (Region of Interest (ROI)) in a mammogram. The method first applies a contrast stretching function to adjust the image contrast, then uses a filtering function to reduce image noise. Next, for each pixel in a ROI, the energy descriptor (one of the Haralick descriptors) is computed from the co-occurrence matrix of the pixel; and the energy texture image of a ROI is obtained. From the energy texture image, the edges in the image are detected; and the mass region is identified from the closed-path edges. Finally, the boundary of the identified mass region is used as the contour of the segmented mass. We applied our method to ROI-marked mammogram images from the Digital Database for Screening Mammography (DDSM). Preliminary results show that the contours detected by our method outline the shape and boundary of a mass much more closely than the ROI markings made by radiologists.