Brain tissue segmentation with the GKA method in MRI

Abstract

A novel method will be proposed to automatically segment the tissue of brain in magnetic resonance (MR) images. The core idea behind this method is the mixed use of Gaussian mixture model and K-means Algorithm (GKA). In this paper, the brain tissue of MR images will be segmented into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) by adopting the GKA method. Both the classic Gaussian Mixture Model (GMM) clustering algorithm and the classic K-means clustering algorithm have its own shortcomings when segmenting the brain tissue. In order to improve the accuracy of segment result, the GKA fusion method has been proposed to obtain the advantages of both GMM and K-means, which is based on the characteristics of brain tissue MR images. The experiments show that the novel method can achieve a better result.