In this paper, MR Brain image segmentation technique based on Modified Gaussian Kernelized Fuzzy C- Means (MGKFCM) clustering approach has been presented. Moreover, in FCM the cluster centroids are selected in a random manner, which may affect the process sometime. Hence, In this proposed method, instead of selecting the cluster centres in a random manner, Histogram technique along with K- Means clustering was used. In general, the MR images are suffered by noise, intensity inhomogeneity and Partial Volume Effect (PVE), primarily the noise has been removed by applying median filtering process. The Fuzzy C-Means (FCM) clustering technique has been proposed to deal with the problem of PVE. The intensity inhomogeneity problem has been handled by modifying the Objective function of the standard Fuzzy C- Means by applying a Gaussian radial basis function with the additive bias field. The result analysis has been carried out with the addition of impulsive and Gaussian noise.

Histon formation, K-means clustering, Modified Gaussian Kernelized FCM, Magnetic Resonance (MR) Brain Image, Noise Analysis

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