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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Veröffentlicht in:Pattern recognition letters Jg. 34; H. 2; S. 163 - 175
Hauptverfasser: Kaur, Prabhjot, Soni, A.K., Gosain, Anjana
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.01.2013
Schlagworte:
Fuzzy c-means
Fuzzy clustering
Intuitionistic fuzzy c-means
RBF kernel based intuitionistic fuzzy c-means
Robust image segmentation
Fuzzy clustering
Robust image segmentation
Intuitionistic fuzzy c-means
RBF kernel based intuitionistic fuzzy c-means
Fuzzy c-means
ISSN:0167-8655, 1872-7344
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Abstract 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.
AbstractList 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.
Author Kaur, Prabhjot
Gosain, Anjana
Soni, A.K.
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Keywords Fuzzy clustering
Robust image segmentation
Intuitionistic fuzzy c-means
RBF kernel based intuitionistic fuzzy c-means
Fuzzy c-means
Language English
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Snippet This paper presents an automatic effective intuitionistic fuzzy c-means which is an extension of standard intuitionisitc fuzzy c-means (IFCM). We present a...
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StartPage 163
SubjectTerms Fuzzy c-means
Fuzzy clustering
Intuitionistic fuzzy c-means
RBF kernel based intuitionistic fuzzy c-means
Robust image segmentation
Title A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images
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