Automated Mammogram Segmentation Using Seed Point Identification and Modified Region Growing Algorithm

Abstract

Segmentation is one of the prominent and crucial steps in any image processing applications. Segmentation subdivides the image into its constituent regions or objects. In this paper we propose a novel automatic segmentation method for extracting portion of breast which contains tumor or abnormalities. The proposed method consists of three different stages. In the initial stage, an automatic seed point identification method is used for locating the center pixel of the abnormal regions in the mammogram images. In the next stage, region of interest around the seed point is extracted using the modified version of region growing algorithm for aggregating pixels around the seed point. Finally, gradient operators are used for identifying boundaries of the segmented region. Using these boundaries, segmented region of the mammogram images are cropped and treated as ROIs that may constitute the tumor/abnormal regions. The segmented ROIs are well in agreement with the abnormality portions that are already identified and labeled by the Radiologists. Average time taken for extracting ROI of one mammogram image is 3.7393 seconds.