Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation

The ´kernel method´ has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, we present a kernel-based fuzzy clustering algorithm that exploits the spatial contextual information in image data. The algorithm is realized by modifying the objective function in the conventional fuzzy c-means algorithm using a kernel-induced distance metric and a spatial penalty term that takes into account the influence of the neighboring pixels on the centre pixel. Experimental results on both synthetic and real MR images show that the proposed algorithm is more robust to noise than the conventional fuzzy image segmentation algorithms.