Iterative Morphology-based Segmentation of Breast Tumors in Ultrasound Images

Ultrasonic detection is currently an effective cancer screening and diagnosis method due to the convenience and harmlessness to human. A set of systems are investigated in this article to pick up the complete tumor outline. After noises in ultrasonic tumor images are removed automatically and areas of different characteristics are distinguished by cutting tumor outlines, images with similar attributes are integrated. Finally the tumor outline is described precisely to facilitate the computer tumor classification. Because ultrasound images often contain a lot of noises, preprocessing removes spot noises by Gaussian filter and select then the appropriate threshold to binarize images. ROD (Rank-ordered Differences) method is applied to calculate the grey level difference between neighbour pixels and the particular pixel to detect pixels contaminated by noises. Images become converged by interactive iteration of two masks of different sizes and a false boundary is obtained after Sobel treatment. Cut the original image into small regions by watershed conversion, label regions and calculate the standard deviation within a region. If the standard deviation is close to the region with the false boundary, the region is considered to be the tumor region.