Classification and Segmentation of MRI Brain Images using Support Vector Machine and Fuzzy C-means Clustering

An early diagnosis of brain disorders is very important for timely treatment such diseases.Several imaging modalities are used to capture the anomalities by obtaining either physiological or morphological information. The scans obtained using as magnetic resonance (MRI) investigated radiologists in order diagnose diseases. However investigations time consuming and might involve errors. In this paper, a fuzzy c-means clustering method MRI image segmentation.The GLCM features from segmented images subsequently mapped PCA space. A support vector machine (SVM) classifier classify taken BRATS-13 images. evaluated employing various performance measures Jaccard index, Dice mean square error (MSE), peak signal noise ratio (PSNR). results show that outperforms existing methods.