A Hybrid Method for Magnetic Resonance Brain Images Classification and Segmentation Using Soft Computing Techniques

Abstract

Nowadays, brain tumor is a serious life-threatening disease that can often be treated with risky surgeries. Various classification and segmentation methods for MR (magnetic resonance) brain images have been proposed, but the expected accuracy value could not be reached so far. In this paper, we proposed a hybrid approach that includes modified fuzzy C-means and artificial neural network (ANN) classifier. It consists of five stages: (a) noise removal, (b) feature extraction, (c) feature selection, (d) classification, and (e) segmentation. Initially, a genetic optimized median filter is used to remove noise present in the input image, and then the essential features are extracted and selected using discrete wavelet transform and principle component analysis algorithms, respectively. The normal and abnormal images are classified using the ANN classifier. Finally, it is processed through a modified fuzzy C-means algorithm to segment the tumor portion separately. The proposed segmentation technique has been tested on the BRATS dataset and produces a sensitivity of 98%, Jaccard index of 97%, specificity of 98%, and accuracy of 95%.