MR Brain Tissue Segmentation Based on Clustering Techniques and Neural Network

Abstract

An effective fully-automatic brain tissue segmentation method based on the integration of clustering techniques and Neural Network (NN) is presented. The method aims to combine the strengths of a number of individual clustering techniques in order to achieve improved segmentation. The method starts by enhancing the image contrast through scaling of the pixel intensity. This is followed by pre-segmenting of the brain image through grouping neighboring pixels of similar intensities into objects. After that, the pixel intensity features are extracted for each group. Then, each object is partitioned using two (or more) different clustering techniques. The training labels are learned by feeding the extracted features and clustering methods’ outputs into a NN model that is guided by the ground truth values (the target segmentation outputs). In the testing phase, the extracted features and clustering methods’ outputs are fed to the trained NN to predict the class of each object. The efficiency of the proposed method is demonstrated on various MR brain images and compared with the base clustering techniques.