Review of Segmentation Methods for Brain Tissue with Magnetic Resonance Images

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Ritu Agrawal 1,* Manisha Sharma 1

1. Bhilai Institute of Technology Durg (C.G.), India

* Corresponding author.


Received: 7 May 2013 / Revised: 10 Sep. 2013 / Accepted: 17 Dec. 2013 / Published: 8 Mar. 2014

Index Terms

Medical Image Segmentation, Adaptive Spatial Fuzzy C – means, Markov Random Field, Fuzzy Connectedness, Brain MRI


Medical Magnetic Resonance Images (MRI) is characterized by a composition of small differences in signal intensities between different tissues types. Thus ambiguities and uncertainties are introduced in image formation. In this paper, review of the current approaches in the tissue segmentation of MR Brain Images has been presented. The segmentation algorithms has been divided into four categories which is able to deal with different intensity non-uniformity as adaptive spatial Fuzzy C - means, Markov Random Field, Fuzzy connectedness method and atlas based re-fuzzy connectedness. The performance of these segmentation methods have been compared in terms of validation metric as dice similarity coefficient, overlap ratio and Jaccard coefficient. The comparison of all validation metric at different levels of intensity non-uniformity shows that adaptive Fuzzy C - means clustering segmentation method give better result in segmentation of brain tissue.

Cite This Paper

Ritu Agrawal, Manisha Sharma, "Review of Segmentation Methods for Brain Tissue with Magnetic Resonance Images", International Journal of Computer Network and Information Security(IJCNIS), vol.6, no.4, pp.55-62, 2014. DOI:10.5815/ijcnis.2014.04.07


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