Hassan Akbari - Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology, Tehran, Iran
Hamed Mohebbi Kalkhoran - Biomedical Signal and Image Processing Laboratory (BiSIPL)Sharif University of Technology, Tehran, Iran
Emad Fatemizadeh - Biomedical Signal and Image Processing Laboratory (BiSIPL)Sharif University of Technology, Tehran, Iran

Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in performing it on artificial synthesized images, and the results show the superior accuracy compared tosome other state of the art FCM-based segmentation methods

Image Segmentation, FCM, Total Variation