Detection of Brain Tumor in MR images using hybrid Fuzzy C-mean clustering with graph cut segmentation technique

Abstract

In the medical image processing field segmentation task is very important process in order to diagnose any diseases in medical images taken from different machine. Medical images from MRI, CT scan, X-rays, Ultrasound and PET have different features, so segmentation process is very challenging task. In this paper, the graph theoretical approaches are proposed because it has flexibility representing any complex structure. Before segmentation process, MR images are pre-processed using Region of Interest, Inverse method and Boundary detection method.   In this method the segmentation of MRI brain images is performed using Fuzzy C-mean clustering with graph cut techniques. FCM algorithm is proved to be efficient in term of computational rate by improving cluster center and modified way to selection of seed points.  Seed point selection is by using FCM clustering derives new technique called Fuzzy C-mean seed selection (FCMSS). Image segmentation is performed using obtained path in the graph applied on set of cluster region of interest on MR images to detect brain tumors.