Comparative study of techniques for brain tumor segmentation

In this paper, we present a comparative study of various techniques which have been proposed for segmentation of brain tumors in MRI data. Three different techniques are discussed in this paper. These include morphological watershed segmentation, K-Means and Fuzzy C-means clustering. In watershed technique, marker is used for tumor segmentation. Clustering is a technique for reducing the number of objects in the data set. K-Means and Fuzzy C-Means clustering algorithms are discussed in this paper. K-Means used an objective function for clustering while Fuzzy C-Means comes under the category of soft segmentation technique. Simulation are dome in MATLAB 2013a and results for the techniques are discussed.