Kernelized type-2 fuzzy c-means clustering algorithm in segmentation of noisy medical images

Abstract

The toughest challenges in medical diagnosis are uncertainty handling and noise. This paper presents a novel kernelized type-2 fuzzy c-means algorithm that is a generalization of conventional type-2 fuzzy c-means (T2FCM). Although T2FCM has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, we present a novel kernelized type-2 fuzzy c-means (KT2FCM) where type-2 fuzzy c-means is extended by adopting a kernel induced metric in the data space to replace the original Euclidean norm metric. Use of kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. From our experiments, we found that different kernel with different kernel widths lead to different clustering results. Thus a key point is to choose an appropriate value for the kernel width. Experimental are done using synthetic and real medical images (CT Scan and MR images) to show the effectiveness of method.