Volume 5 Number 5 (Oct. 2013)
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IJCEE 2013 Vol.5(5): 438-441 ISSN: 1793-8163
DOI: 10.7763/IJCEE.2013.V5.748

Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-Means

Soodabeh Safa, Behrouz Bokharaeian, and Ali Soleymani
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.

Index Terms—Brain MRI segmentation, fuzzy expected maximization, histogram based k-mean.

Soodabeh Safa is with the University Putra Malaysia, Selangor, 43400 Malaysia (e-mail: soodabeh_safa@yahoo.com).
Behrouz Bokharaeian is with Complutense University de Madrid, 28040 Spain (e-mail: bokharaeian@gmail.com).
Ali Soleymani is with the Faculty of Information and Science Technology, Universiti Kebangsaan Malaysia, Selangor, 43600 Malaysia (e-mail: ali.soleymani@gmail.com).

Cite:Soodabeh Safa, Behrouz Bokharaeian, and Ali Soleymani, "Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-Means," International Journal of Computer and Electrical Engineering vol. 5, no. 5, pp. 438-441, 2013.

General Information

ISSN: 1793-8163
Frequency: Semiyearly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, Engineering & Technology Digital Library, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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