Improved Fuzzy-c-means for Noisy Image Segmentation

Magnetic resonance (MR) imaging is an important diagnostic imaging technique to early detect abnormal changes in the bain tissues. However, a serious limitation of the MR images is the significant amount of noise which can lead to inaccuracte segmentation. In this paper, a robust segmentation method based on an improvement of the conventional Fuzzy-C-Means (FCM) by modifiying its membership function is realized. A neighborhood attraction depending on the relative location and features of neighboring pixels is incorporated into the membership function to make the method robust to noise. Simulated and real brain MR images with different noise levels are used to demonstrate the superiority of the proposed method compared to some other FCM-based methods.