Open AccessRough intuitionistic type‐2 fuzzy c‐means clustering algorithm for MR image segmentation
Author(s) -
Chen Xiangjian,
Li Di,
Wang Xun,
Yang Xibei,
Li Hongmei
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5597
Subject(s) - cluster analysis , image segmentation , pattern recognition (psychology) , type (biology) , artificial intelligence , segmentation , computer science , fuzzy clustering , fuzzy logic , image (mathematics) , algorithm , mathematics , ecology , biology
In recent years, the clinical application of magnetic resonance (MR) images is more and more extensive and in‐depth. However, image segmentation is a bottleneck to restrict the application of MR imaging in clinic, and the segmentation of brain MR images now is confronted with the presence of uncertainty and noise, and various kinds of algorithms have been proposed to handle this problem. In this study, a hybrid clustering algorithm combined with a new intuitionistic fuzzy factor and local spatial information is proposed, where type‐2 fuzzy logic can handle randomness, the rough set can deal with vagueness, and the intuitionistic fuzzy logic can address the external noises. Finally, the experimental tests have been done to demonstrate the superiority of the proposed technique.