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Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-MeansDOI: 10.7763/ijcee.2013.v5.748 Keywords: Brain MRI segmentation , fuzzy expected maximization , histogram based k-mean Abstract: 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.
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