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Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Authors:

Daoqiang ZhangAuthors Info & Claims

Pattern Recognition, Volume 40, Issue 3

Pages 825 - 838

https://doi.org/10.1016/j.patcog.2006.07.011

Published: 01 March 2007 Publication History

Abstract

Fuzzy c-means (FCM) algorithms with spatial constraints (FCM_S) have been proven effective for image segmentation. However, they still have the following disadvantages: (1) although the introduction of local spatial information to the corresponding objective functions enhances their insensitiveness to noise to some extent, they still lack enough robustness to noise and outliers, especially in absence of prior knowledge of the noise; (2) in their objective functions, there exists a crucial parameter @a used to balance between robustness to noise and effectiveness of preserving the details of the image, it is selected generally through experience; and (3) the time of segmenting an image is dependent on the image size, and hence the larger the size of the image, the more the segmentation time. In this paper, by incorporating local spatial and gray information together, a novel fast and robust FCM framework for image segmentation, i.e., fast generalized fuzzy c-means (FGFCM) clustering algorithms, is proposed. FGFCM can mitigate the disadvantages of FCM_S and at the same time enhances the clustering performance. Furthermore, FGFCM not only includes many existing algorithms, such as fast FCM and enhanced FCM as its special cases, but also can derive other new algorithms such as FGFCM_S1 and FGFCM_S2 proposed in the rest of this paper. The major characteristics of FGFCM are: (1) to use a new factor S"i"j as a local (both spatial and gray) similarity measure aiming to guarantee both noise-immunity and detail-preserving for image, and meanwhile remove the empirically-adjusted parameter @a; (2) fast clustering or segmenting image, the segmenting time is only dependent on the number of the gray-levels q rather than the size N(@__ __q) of the image, and consequently its computational complexity is reduced from O(NcI"1) to O(qcI"2), where c is the number of the clusters, I"1 and I"2(

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Guo QSong HWang CFan JAi DGao YYu XYang J(2024)Segmentation of 3D Anatomically Diffused Tissues in Magnetic Resonance Images Through Edge-Preserving Constrained Center-Free Fuzzy $C$-MeansIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.337350932:6(3444-3457)Online publication date: 8-Mar-2024
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Wang CTan XLi BZhou M(2024)Noise-Estimation-Dominated Fuzzy Segmentation Strategy for Accurate Implantation of Implantable Cardioverter DefibrillatorsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.336497032:9(4902-4911)Online publication date: 1-Sep-2024
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Li HWang J(2024)From Soft Clustering to Hard Clustering: A Collaborative Annealing Fuzzy $c$-Means AlgorithmIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.331966332:3(1181-1194)Online publication date: 1-Mar-2024
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Index Terms

Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Pattern matching

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 40, Issue 3

March, 2007

390 pages

ISSN:0031-3203

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Copyright © Pattern Recognition Society © 2006.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 March 2007

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Guo QSong HWang CFan JAi DGao YYu XYang J(2024)Segmentation of 3D Anatomically Diffused Tissues in Magnetic Resonance Images Through Edge-Preserving Constrained Center-Free Fuzzy $C$-MeansIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.337350932:6(3444-3457)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3373509
Wang CTan XLi BZhou M(2024)Noise-Estimation-Dominated Fuzzy Segmentation Strategy for Accurate Implantation of Implantable Cardioverter DefibrillatorsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.336497032:9(4902-4911)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3364970
Li HWang J(2024)From Soft Clustering to Hard Clustering: A Collaborative Annealing Fuzzy $c$-Means AlgorithmIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.331966332:3(1181-1194)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3319663
Yan XJin ZMao YYe YYu H(2024)Differentiable self-supervised clustering with intrinsic interpretabilityNeural Networks10.1016/j.neunet.2024.106542179:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106542
Cui HXie ZZeng WMa RZhang YYin QXu Z(2024)Intuitionistic fuzzy local information C-means algorithm for image segmentationInformation Sciences: an International Journal10.1016/j.ins.2024.121205681:COnline publication date: 1-Oct-2024
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Farooq AMemon K(2024)Kernel possibilistic fuzzy c-means clustering algorithm based on morphological reconstruction and membership filteringFuzzy Sets and Systems10.1016/j.fss.2023.108792477:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.fss.2023.108792
Wu JWang XLiu YFang C(2024)Adaptive sparse regularized fuzzy clustering noise image segmentation algorithm based on complementary spatial informationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124943256:COnline publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124943
Song SJia ZShi FWang JNi D(2024)Adaptive fuzzy weighted C-mean image segmentation algorithm combining a new distance metric and prior entropyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107776131:COnline publication date: 1-May-2024
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Singh CRanade SKaur DBala A(2024)A kernelized-bias-corrected fuzzy C-means approach with moment domain filtering for segmenting brain magnetic resonance imagesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09379-z28:3(1909-1933)Online publication date: 1-Feb-2024
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Wu CPeng SZhang X(2024)Robust interval type-2 kernel-based possibilistic fuzzy deep local information clustering driven by Lambert-W functionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02910-140:3(2161-2201)Online publication date: 1-Mar-2024
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