Edge detection of mammary calcifications based on ant colony algorithm and fuzzy clustering

Detection of mammary calcifications is challenged in computer-aided diagnosis. Maximum grade of each pixel of digital image of mammograph is calculated. Pixels are divided into different group and all pixels in each group have the same value of grade. Initial clustering centers are obtained through that grade area is divided into n equivalent parts. New clustering centers are obtained through simulating ant's search food behavior in which cluster pixels group work according to probability selection principle. New clustering results are obtained through fuzzy C-means by using the above clustering centers as initialized centers. The experimental results demonstrate that the algorithm is effective to obtain edges of calcifications by choosing appropriate parameters.