Noise robust intuitionistic fuzzy c-means clustering algorithm incorporating local information
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This article is published in Iet Image Processing.The article was published on 2021-02-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Noise & Cluster analysis.read more
Citations
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Proceedings ArticleDOI
Fuzzy C-Means Based CAD Sytem for Liver Tumors Segmentation from CT Scans
TL;DR: In this paper , the authors proposed a CADe system framework to automatically segment the liver along with liver tumors using Fast-Generalized Fuzzy C-Means (FG-FCM) and Kernel-Based C-means (KFCM), respectively.
Proceedings ArticleDOI
Fuzzy C-Means Based CAD Sytem for Liver Tumors Segmentation from CT Scans
TL;DR: In this paper , the authors proposed a CADe system framework to automatically segment the liver along with liver tumors using Fast-Generalized Fuzzy C-Means (FG-FCM) and Kernel-Based C-means (KFCM), respectively.
Journal ArticleDOI
Research and improvement of C-means clustering algorithm based on Image segmentation application
Chunying Wang,Qing Yang +1 more
TL;DR: In this paper , a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably, and the non-local spatial information coefficient is introduced to improve the identification ability of the influence factors.
References
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Journal ArticleDOI
Intuitionistic fuzzy sets
TL;DR: Various properties are proved, which are connected to the operations and relations over sets, and with modal and topological operators, defined over the set of IFS's.
Journal ArticleDOI
Distances between intuitionistic fuzzy sets
Eulalia Szmidt,Janusz Kacprzyk +1 more
TL;DR: It is shown that all three parameters describing intuitionistic fuzzy sets should be taken into account while calculating those distances between intuitionistically fuzzy sets.
Journal ArticleDOI
Performance evaluation of some clustering algorithms and validity indices
TL;DR: This article evaluates the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, andA recently developed index I.
Journal ArticleDOI
Cluster Validity with Fuzzy Sets
TL;DR: This paper uses membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X.
Journal ArticleDOI
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure
Songcan Chen,Daoqiang Zhang +1 more
TL;DR: Two variants of fuzzy c-means clustering with spatial constraints, using the kernel methods, are proposed, inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering theNon-E Euclidean structures in data.