ROI Segmentation from Brain MR Images with a Fast Multilevel Thresholding

Abstract

A novel region of interest (ROI) segmentation for detection of Glioblastoma multiforme (GBM) tumor in magnetic resonance (MR) images of the brain is proposed using a two-stage thresholding method. We have defined multiple intervals for multilevel thresholding using a novel meta-heuristic optimization technique called Discrete Curve Evolution. In each of these intervals, a threshold is selected by bi-level Otsu’s method. Then the ROI is extracted from only a single seed initialization, on the ROI, by the user. The proposed segmentation technique is more accurate as compared to the existing methods. Also the time complexity of our method is very low. The experimental evaluation is provided on contrast-enhanced T1-weighted MRI slices of three patients, having the corresponding ground truth of the tumor regions. The performance measure, based on Jaccard and Dice indices, of the segmented ROI demonstrated higher accuracy than existing methods.