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.
