Open AccessNoise robust intuitionistic fuzzy c‐means clustering algorithm incorporating local information
Author(s) -
Yang Zhenzhen,
Xu Pengfei,
Yang Yongpeng,
Kang Bin
Publication year - 2021
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/ipr2.12064
Subject(s) - noise (video) , computer science , artificial intelligence , fuzzy logic , pattern recognition (psychology) , fuzzy set , cluster analysis , algorithm , segmentation , image (mathematics)
The human brain magnetic resonance image (MRI) is always contaminated by noise and has uncertainty on the boundary between different tissues. These characteristics bring challenges to the human brain image segmentation. To handle these limitations, many variants of standard fuzzy c‐means (FCM) algorithm have been proposed. Some methods attempt to incorporate the local spatial information in the standard FCM algorithm. However, they can't solve the problem of data uncertainty very well. And some other methods can handle the problem of data uncertainty, but they are sensitive to noise since it doesn't incorporate any local spatial information. In this paper, we propose a noise robust intuitionistic fuzzy c‐means (NR‐IFCM) algorithm, which can handle noise and uncertainty problems simultaneously. In order to process the human brain MRI with noise better, we introduce a noise robust intuitionistic fuzzy set (NR‐IFS) which is noise robust in this NR‐IFCM algorithm. Meanwhile, in order to handle the data uncertainty, we also introduce a new intuitionistic fuzzy factor to this NR‐IFCM algorithm which combine the local gray‐level and the spatial information together. A large number of experimental results on human brain MRI validate the effectiveness and the superiority of our proposed NR‐IFCM algorithm.