Fuzzy C-Means Algorithm Based on Gaussian Function for Magnetic Resonance Images (MRIs) Segmentation

In this paper, we propose fuzzy c-means (FCM) method based on Gaussian function for improving magnetic resonance imaging (MRI) segmentation. The proposed algorithm is formulated by modifying the objective function of the standard FCM algorithm to allow the labeling of a pixel to be influenced by other pixels to suppress the noise effect during segmentation processes. The proposed algorithm is feed by the initial centers for the objective function as a prior knowledge to avoid the coincident clusters. Then, the process of finding the best clusters are continue to update the centers and the membership and only stop when the factor between two successive centers is smallest than a prescribed value. The proposed algorithm is applied to magnetic resonance image (MRI) datasets. Compared with the existing approaches, the proposed method can achieve the best accurate results.