A novel efficient kernelized fuzzy C-means with additive bias field for brain image segmentation

In this paper, a suitable novel algorithm has been proposed for segmenting the brain magnetic resonance imaging (MRI) data using an efficient kernelized fuzzy c-means (EKFCM) with spatial constraints. In this proposed algorithm, the Euclidean distance in the standard fuzzy c-means (FCM) is replaced by a Gaussian radial basis function with additive bias. The proposed method will segment the given MRI data automatically, by considering the effects of intensity inhomogeneity, partial volume and noise. The neighbourhood effect acts as a regularizer, and the regularization term is useful in segmenting the MR Imaging corrupted by noise and intensity inhomogeneity. Experimental results on both real and simulated images, prove that the proposed algorithm has higher segmenting accuracy than other segmenting techniques.

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