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Soft Computing
Published in: Soft Computing 2/2015

01-02-2015 | Methodologies and Application

Intuitionistic fuzzy \(c\)-means clustering algorithm with neighborhood attraction in segmenting medical image

Authors: Ching-Wen Huang, Kuo-Ping Lin, Ming-Chang Wu, Kuo-Chen Hung, Gia-Shie Liu, Chih-Hung Jen

Published in: Soft Computing | Issue 2/2015

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Abstract

Fuzzy segmentation methods, especially fuzzy \(c\)-means algorithms, have been widely used in medical imaging in past decades. This paper proposes a novel neighborhood intuitionistic fuzzy \(c\)-means clustering algorithm with a genetic algorithm (NIFCMGA). This new clustering algorithm technology can retain the advantages of an intuitionistic fuzzy \(c\)-means clustering algorithm to maximize benefits and reduce noise/outlier influences through neighborhood membership. Furthermore, the genetic algorithms were used simultaneously to select the optimal parameters of the proposed clustering algorithm. This proposed technology has been successfully applied to the clustering of different regions of magnetic resonance imaging and computerized tomography scanning, which may be extended to the diagnosis of abnormalities. Comparisons with other approaches demonstrate the superior performance of the proposed NIFCMGA.
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Metadata
Title
Intuitionistic fuzzy -means clustering algorithm with neighborhood attraction in segmenting medical image
Authors
Ching-Wen Huang
Kuo-Ping Lin
Ming-Chang Wu
Kuo-Chen Hung
Gia-Shie Liu
Chih-Hung Jen
Publication date
01-02-2015
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 2/2015
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1264-2

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