Review of Segmentation Methods for Brain Tissue with Magnetic Resonance Images

Review of Segmentation Methods for Brain Tissue with Magnetic Resonance Images

Автор: Ritu Agrawal, Manisha Sharma

Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis

Статья в выпуске: 4 vol.6, 2014 года.

Бесплатный доступ

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.

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

Короткий адрес: https://sciup.org/15011295

IDR: 15011295

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