NDLI: Intuitionistic fuzzy $$c$$ -means clustering algorithm with neighborhood attraction in segmenting medical image

Content Provider	Springer Nature Link
Author	Huang, Ching Wen Lin, Kuo Ping Wu, Ming Chang Hung, Kuo Chen Liu, Gia Shie Jen, Chih Hung
Copyright Year	2014
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.
Starting Page	459
Ending Page	470
Page Count	12
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	19
Issue Number	2
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-03-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy segmentation Fuzzy $$c$$ -means Medical images Neighborhood intuitionistic fuzzy $$c$$ -means clustering algorithm Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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