JIP_V2_N4_RP3 T. Chandra Sekhar Rao G. Sreenivasulu Journal on Image Processing 2349-6827 2 4 19 29 CAD, MRI, Brain Tumor, Edge Analysis, K means Segmentation, FCM, EICA Medical image processing has become the main stay of diagnosis for a multitude of diseased conditions. The advent of sophisticated image processing procedures coupled with the exponential growth in processing power of systems and storages has resulted in huge volume of data that has to be interpreted and analyzed. The huge volume data implies the need for automated analysis system to reduce the burden on radiologists and help in providing quality diagnosis. This paper presents a Computer Aided Diagnosis (CAD) system for segmentation and analysis of Brain tumors in magnetic resonance images. The system has scope for making different analysis like edge analysis, morphological processing, histogram analysis etc. As part of system four different segmentation approaches like, K means segmentation, Watershed segmentation; Fuzzy C Means (FCM) segmentation and Enhanced Independent Component Analysis (EICA) and Mixture model based segmentation are implemented. The performance of the segmentation approaches are evaluated using different performance measures. October - December 2015 Copyright © 2015 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=3687