Magnetic Resonance Images are used in detecting and tracking brain tumors. Diagnostic brain MRI scans are usually performed by trained medical technologists who manually prescribe the position and orientation of a scanning volume. T2-weighted scans use a Spin Echo (SE) sequence, with long TE (Echo time) and long TR (repetition time).On a T2-weighted scan, water- and fluid-containing tissues are bright and fat containing tissues are dark. Medical image segmentation plays an important role in medical research field. In this work we are going to compare the effectiveness of three segmentation algorithms named as FCM (Fuzzy C-Means) and PSO (Particle Swarm Optimization). The effectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updating criterion. PSO will identify a subset of the search space (the boundary) with specific value. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. In this we also made these two algorithms as a hybrid in manner. Finally, among these two algorithms the best one is selected by computing the measures such as entropy, MSE, PSNR and segmentation accuracy.