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Open AccessArticle

Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

by 1,2, 1,2, 1,2,* and 1,2
1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 696; https://doi.org/10.3390/s21030696
Received: 18 December 2020 / Revised: 15 January 2021 / Accepted: 18 January 2021 / Published: 20 January 2021
(This article belongs to the Section Sensing and Imaging)
The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy. View Full-Text
Keywords: image segmentation; information transferring; fuzzy C-means algorithm; similarity image segmentation; information transferring; fuzzy C-means algorithm; similarity
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MDPI and ACS Style

Chen, H.; Xie, Z.; Huang, Y.; Gai, D. Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement. Sensors 2021, 21, 696. https://doi.org/10.3390/s21030696

AMA Style

Chen H, Xie Z, Huang Y, Gai D. Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement. Sensors. 2021; 21(3):696. https://doi.org/10.3390/s21030696

Chicago/Turabian Style

Chen, Haipeng; Xie, Zeyu; Huang, Yongping; Gai, Di. 2021. "Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement" Sensors 21, no. 3: 696. https://doi.org/10.3390/s21030696

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