Paper Title
Segmentation and Classification For Brain MRI Image Based ON Modified FCM With Zernike Moment Classifier

Automatic segmentation of brain tissues from MRI is of great importance for clinical application and scientific research. We propose a robust discriminative segmentation method from the view of information theoretic learning. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. The effectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updating criterion. In this paper, the application of modified FCM algorithm for MR brain tumor detection is explored. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Index terms- discriminative segmentation, fuzzy C-means (FCM) algorithm, modified FCM algorithm, zernike moment classfier.