%0 Journal Article
%T Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-Means
%A Soodabeh Safa
%A Behrouz Bokharaeian
%A Ali Soleymani
%J International Journal of Computer and Electrical Engineering
%D 2013
%I IACSIT Press
%R 10.7763/ijcee.2013.v5.748
%X Expectation maximization algorithm has been extensively used in a variety of medical image processing applications, especially for detecting human brain disease. In this paper, an efficient and improved semi-automated Fuzzy EM based techniques for 3-D MR segmentation of human brain images is presented. FEM along with histogram based K-means in initialization step is used for the labeling of individual pixels/voxels of a 3D anatomical MR image (MRI) into the main tissue classes in the brain, Gray matter (GM), White matter (WM), CSF (Celebro-spinal fluid). FEM¡®s membership function were estimated through a histogram-based method. The results show our proposed FEM-KMeans has better performance and convergence speed compare to histogram based EM.
%K Brain MRI segmentation
%K fuzzy expected maximization
%K histogram based k-mean
%U http://www.ijcee.org/papers/748-A259.pdf