caa a22 4500 605470618 CHVBK 20210128100329.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00500-014-1264-2 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1264-2 Intuitionistic fuzzy $$c$$ c -means clustering algorithm with neighborhood attraction in segmenting medical image [Elektronische Daten] [Ching-Wen Huang, Kuo-Ping Lin, Ming-Chang Wu, Kuo-Chen Hung, Gia-Shie Liu, Chih-Hung Jen] Fuzzy segmentation methods, especially fuzzy $$c$$ c -means algorithms, have been widely used in medical imaging in past decades. This paper proposes a novel neighborhood intuitionistic fuzzy $$c$$ c -means clustering algorithm with a genetic algorithm (NIFCMGA). This new clustering algorithm technology can retain the advantages of an intuitionistic fuzzy $$c$$ 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. Springer-Verlag Berlin Heidelberg, 2014 Fuzzy segmentation nationallicence Fuzzy $$c$$ c -means nationallicence Medical images nationallicence Neighborhood intuitionistic fuzzy $$c$$ c -means clustering algorithm nationallicence Huang Ching-Wen Department of Diagnostic Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Shihlin, 111, Taipei, Taiwan aut Lin Kuo-Ping Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut Wu Ming-Chang Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut Hung Kuo-Chen Department of Computer Science and Information Management, Hungkuang University, 433, Taichung, Taiwan aut Liu Gia-Shie Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut Jen Chih-Hung Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 459-470 1432-7643 19:2<459 2015 19 500 https://doi.org/10.1007/s00500-014-1264-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1264-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huang Ching-Wen Department of Diagnostic Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Shihlin, 111, Taipei, Taiwan aut NATIONALLICENCE 700 1- Lin Kuo-Ping Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut NATIONALLICENCE 700 1- Wu Ming-Chang Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut NATIONALLICENCE 700 1- Hung Kuo-Chen Department of Computer Science and Information Management, Hungkuang University, 433, Taichung, Taiwan aut NATIONALLICENCE 700 1- Liu Gia-Shie Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut NATIONALLICENCE 700 1- Jen Chih-Hung Department of Information Management, Lunghwa University of Science and Technology, 333, Taoyuan, Taiwan aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 459-470 1432-7643 19:2<459 2015 19 500