Fuzzy clustering based applications to medical image segmentation

Medical image segmentation is an indispensable process in the visualization of human tissues. However, medical images always contain a large amount of noise caused by operator performance, equipment and environment. This leads to inaccuracy with segmentation. A robust segmentation technique is required. In this paper, based on the traditional fuzzy c-means (FCM) clustering algorithm, the neighborhood attraction is shown to improve the segmentation performance. Two factors of the neighborhood attraction depend on relative location and features of neighboring pixels in the image. Simulated and real brain magnetic resonance (MR) images are segmented to demonstrate the superiority of the proposed method compared to the conventional FCM method.