A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images
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...
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| Published in: | Pattern recognition letters Vol. 34; no. 2; pp. 163 - 175 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
15.01.2013
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| Subjects: | |
| ISSN: | 0167-8655, 1872-7344 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 0167-8655 1872-7344 |
| DOI: | 10.1016/j.patrec.2012.09.015 |