Segmentation of breast masses using adaptive region growing

Since there are a lot of complex and changing characteristics of mass in mammography with great difficulty in mass segmentation, region growing become a reliable method to accomplish it. An adaptive region growing method for mass segmentation is presented so as to improve its precision and reliability and reduce the over-growing and lack-growing when dealing with different images in one principle. Background removing and region suppression are used to preprocessing the area of interest (ROI) of mass, and then it use the number of image pixels to determine the seed point for region growing, and determine whether the adaptive region growing is out of edge though the gradient distribution and tends of mass ROI in order to obtain the best growth criteria. The experimental results show that the adaptive region growing algorithm for segmentation compared to the three-terrain segmentation algorithm and model segmentation algorithm is more accurate and reliable.