A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images (Retracted article. See vol. 34, pg. 709, 2013)

作者：Kaur Prabhjot^*; Soni A K; Gosain Anjana

来源：Pattern Recognition Letters, 2013, 34(2): 163-175.

DOI：10.1016/j.patrec.2012.09.015

• Fuzzy clustering
• Intuitionistic fuzzy c-means
• Robust image segmentation
• RBF kernel based intuitionistic fuzzy c-means
• Fuzzy c-means

This paper presents an automatic effective intuitionistic fuzzy c-means which is an extension of standard intuitionisitc fuzzy c-means (IFCM). We present a model called RBF Kernel based intuitionistic fuzzy c-means (KIFCM) where IFCM is extended by adopting a kernel induced metric in the data space to replace the original Euclidean norm metric. By using kernel function it becomes possible to cluster data, which is linearly non-separable in the original space, into homogeneous groups by transforming the data into high dimensional space. Proposed clustering method is applied on synthetic data-sets referred from various papers, real data-sets from Public Library UCI, Simulated and Real MR brain images. Experimental results are given to show the effectiveness of proposed method in contrast to conventional fuzzy c-means, possibilistic c-means, possibilistic fuzzy c-means, noise clustering, kernelized fuzzy c-means, type-2 fuzzy c-means, kernelized type-2 fuzzy c-means, and intuitionistic fuzzy c-means.

• 出版日期2013-1-15