Comparative Analysis of Automatic Brain Tumor Segmentation Techniques Using Watershed, Region Growing and K-Means Clustering

In the wide area of medical image processing, Image segmentation is an important and challenging factor in automatic recognition system for medical images. Medical images acquired from different resources such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc that will be used for the diagnosis purpose. The purpose of this paper is to provide a comprehensive analysis for different brain tumor segmentation methods. Comparative analysis among these various segmentation conventions has been examined. This paper discusses the various image segmentation techniques K-Means Clustering, Watershed and Region Growing methods for detection of brain tumor from sample MRI images of brain.