article

An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation

Authors:

Kang SimAuthors Info & Claims

Computer Vision and Image Understanding, Volume 117, Issue 10

Pages 1412 - 1420

https://doi.org/10.1016/j.cviu.2013.05.001

Published: 01 October 2013 Publication History

Abstract

This paper presents an adaptive spatial information-theoretic fuzzy clustering algorithm to improve the robustness of the conventional fuzzy c-means (FCM) clustering algorithms for image segmentation. This is achieved through the incorporation of information-theoretic framework into the FCM-type algorithms. By combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the problems of sensitivity to noisy data and the lack of spatial information, and improve the image segmentation results. The experimental results have shown that this robust clustering algorithm is useful for MRI brain image segmentation and it yields better segmentation results when compared to the conventional FCM approach.

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Cited By

Chen JZhu JJiang HYang HNie F(2023)Sparsity Fuzzy C-Means Clustering With Principal Component Analysis EmbeddingIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.321734331:7(2099-2111)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2022.3217343
Kalti KTouil A(2023)A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image SegmentationJournal of Classification10.1007/s00357-023-09443-140:3(488-512)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s00357-023-09443-1
Li DWang SHe QYang Y(2022)Cost-effective land cover classification for remote sensing imagesJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00335-011:1Online publication date: 4-Oct-2022
https://dl.acm.org/doi/10.1186/s13677-022-00335-0
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An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image Computer Vision and Image Understanding

Computer Vision and Image Understanding Volume 117, Issue 10

October, 2013

344 pages

ISSN:1077-3142

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Copyright © Elsevier Inc. © 2013.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 October 2013

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Cited By

Chen JZhu JJiang HYang HNie F(2023)Sparsity Fuzzy C-Means Clustering With Principal Component Analysis EmbeddingIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.321734331:7(2099-2111)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2022.3217343
Kalti KTouil A(2023)A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image SegmentationJournal of Classification10.1007/s00357-023-09443-140:3(488-512)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s00357-023-09443-1
Li DWang SHe QYang Y(2022)Cost-effective land cover classification for remote sensing imagesJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00335-011:1Online publication date: 4-Oct-2022
https://dl.acm.org/doi/10.1186/s13677-022-00335-0
Khatri IKumar DGupta A(2022)A noise robust kernel fuzzy clustering based on picture fuzzy sets and KL divergence measure for MRI image segmentationApplied Intelligence10.1007/s10489-022-04315-453:13(16487-16518)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1007/s10489-022-04315-4
Jyothi PSingh A(2022)Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a reviewArtificial Intelligence Review10.1007/s10462-022-10245-x56:4(2923-2969)Online publication date: 16-Aug-2022
https://dl.acm.org/doi/10.1007/s10462-022-10245-x
Kumar DKhatri IGupta AGusain R(2022)Kernel picture fuzzy clustering with spatial neighborhood information for MRI image segmentationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07269-426:22(12717-12740)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00500-022-07269-4
Bai XChen ZLiu MZhang Y(2021)Center-free PFCM for MRI brain image segmentation2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7350880(656-660)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/ICIP.2015.7350880
Kala RDeepa P(2021)Spatial Rough Intuitionistic Fuzzy C-Means Clustering for MRI SegmentationNeural Processing Letters10.1007/s11063-021-10441-w53:2(1305-1353)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11063-021-10441-w
Deb DRoy S(2021)Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimizationMultimedia Tools and Applications10.1007/s11042-020-09810-980:2(2621-2645)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09810-9
Das SChakravortty S(2021)Efficient entropy-based spatial fuzzy c-means method for spectral unmixing of hyperspectral imageSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05697-225:11(7379-7397)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s00500-021-05697-2
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