Comparison Study of Segmentation Techniques for Brain Tumour Detection?

Image segmentation plays an important role in diagnosis and treatment of diseases. Image segmentation locates objects and boundaries with in images and the segmentation process is stopped when region of interest is separated from the input image. Based on the application, region of interest may differ and hence none of the segmentation algorithm satisfies the global applications. Thus segmentation still remains a challenging area for researchers. This paper emphasis on comparison study of segmentation techniques for segmenting brain tumour from MRI images. The tumour area is identified by using different algorithms like seeded region growing and merging, K-Means, KNN, fuzzy C-Means and a comparative study of all this methods is presented here.