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

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 tum...

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Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 3; s. 696
Hlavní autori: Chen, Haipeng, Xie, Zeyu, Huang, Yongping, Gai, Di
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 20.01.2021
MDPI
Predmet:
Accuracy
Algorithms
Brain cancer
Brain research
Clustering
fuzzy C-means algorithm
Fuzzy sets
image segmentation
information transferring
Neighborhoods
Noise
similarity
Tumors
fuzzy C-means algorithm
image segmentation
information transferring
similarity
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21030696