Off recent level set methods have been used widely in medical image processing. This paper focuses on level set and its variation for MRI brain tissue segmentation. The different tissues are white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) in brain image. It is difficult to differentiate the boundaries for these tissues. A spatial fuzzy c means and level set segmentation methodology are adopted in this paper for brain MRI image segmentation into WM, GM, and CSF. Initially, segmentation is performed by using SFCM and level sets are used on the result of SFCM. The performance of SFCM and level sets is appraised on Brain Web Database where T1, T2, and ρ weighted images are chosen, whose thickness is 5mm with different intensity nonuniformity (RF) and noise. Experimental results demonstrate the supremacy of segmentation precision even on the noisy MRI brain image. The accuracy, sensitivity, and specificity are improved with better segmentation.

Image segmentation, Level set, SFCM

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