Seeded region growing algorithm is an automated segmentation method in which the region of interest begins as a single pixel and grows based on surrounding pixels with similar

For automatic breast cancer detection, mass segmentation is and continues to be a major challenge. The segmentation objective is to separate the mass from the rest of the breast by trying to delimit its borders correctly. Seeded Region Growing technique is very attractive for medical image segmentation by involving the high-level knowledge of image components in the seed selection procedure. This algorithm starts by a seed point selection and, grows seed area by exploiting the fact that pixels which are close together have similar features. In region growing process the choice of the seed point is very crucial because the overall success of the segmentation is dependent on it. In this paper, we present a comparative study of two automatic seed selection methods for breast tumor detection using seeded region growing segmentation. The first method is based on edges extraction technique; the second method is based on features extraction technique. Our results showed that seed selection method based on features extraction technique is better than seed selection method based on edges extraction technique. Keywords— mammograms, mass detection, seed selection, segmentation, region growing.