Performance Analysis of Entropy based methods and Clustering methods for Brain Tumor Segmentation

Brain tumor is the most deadly disease that affects human life span. To segment the brain tumor part, many segmentation techniques have been emerged in image processing like region based Segmentation, Boundary based segmentation. In this paper, several entropies based methods and several cluster techniques are compared and analyzed for brain tumor segmentation. Several entropies such as rough entropy, Shannon entropy, Renyi entropy, Min entropy, Log Energy, entropy and several clustering methods such as K-Means segmentation, Fuzzy C-Means segmentation and improved Fuzzy CMeans clustering based on measure of medium truth degree are applied for segmentation of brain tumor MRI image and the result is compared and analyzed. Entropy and clustering methods are applied to segment the different parts of the image based on threshold. The proposed segmentation gives higher accuracy when compared with other methods like Region based segmentation, pixel based segmentation. Image accuracy is calculated using Peak Signal noise ratio (PSNR), Mean square error (MSE) for each entropy method and for each clustering method and the results show that Rough entropy gives better result for segmentation. Keywords-Filter, Rough Entropy, Shannon entropy, Renyi Entropy, Min Entropy, Log Energy Entropy, K-Means, Fuzzy C-Means, Improved Fuzzy C-Means Clustering, PSNR, MSE