Image segmentation has been an intriguing area for research and developing efficient algorithms, playing a paramount role in high caliber image interpretation and image analysis. Segmentation of images plays an imperative role in medical diagnosis. Such segmentation demands a robust segmentation algorithm against noise. The legendary orthodox fuzzy c-means algorithm is proficiently exploited for clustering in medical image segmentation. FCM is highly sensitive to noise due to the practice of only intensity values for clustering. Thus this paper aims to apply the 'kernel method', instituted on the conventional fuzzy clustering algorithm (FCM) to swap the Euclidean metric norm to a novel kernel-induced metric in the data space. Images can be segmented by pixel classification through clustering of all features of interest. In unsupervised methods of clustering algorithms utilizing kernel method, a nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then clustering is executed. The integer of clusters in the multidimensional feature space thus represents the number of classes in the image. As the image is sorted into cluster classes, segmented regions are obtained by examination of the neighborhood pixels for the same class label. Since clustering produces disjointed regions with holes or regions with a single pixel, a post processing algorithm such as region growing, pixel connectivity, or a rule-based algorithm is applied to obtain the final segmented regions.

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Segmentation Clustering Kernel Nonlinear Fcm