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 Kmeans 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.
Index Terms—Brain MRI segmentation; fuzzy expected maximization; histogram based k-mean.
Authors are with Faculty of Computer Science and Information Technology University Putra Malaysia Selangor, Malaysia (e-mail: Soodabeh_safa@yahoo.com, firstname.lastname@example.org).
Cite: Soodabeh Safa and Behrouz Bokharaeian, "New methods in Brain MR Segmentation with Fuzzy EM algorithm," International Journal of Information and Education Technology vol. 1, no. 4, pp. 280-285, 2011.