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

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• 作者：Ching-Wen Huang (1)
  Kuo-Ping Lin (2)
  Ming-Chang Wu (2)
  Kuo-Chen Hung (3)
  Gia-Shie Liu (2)
  Chih-Hung Jen (2)
  
  1. Department of Diagnostic Radiology ; Shin Kong Wu Ho-Su Memorial Hospital ; Shihlin ; Taipei ; 111 ; Taiwan
  2. Department of Information Management ; Lunghwa University of Science and Technology ; Taoyuan ; 333 ; Taiwan
  3. Department of Computer Science and Information Management ; Hungkuang University ; Taichung ; 433 ; Taiwan
  
• 关键词：Fuzzy segmentation ; Fuzzy $$c$$ c ; means ; Medical images ; Neighborhood intuitionistic fuzzy $$c$$ c ; means clustering algorithm
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：19
• 期：2
• 页码：459-470
• 全文大小：2,469 KB
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• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

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.