Image segmentation based on fast kernelized fuzzy clustering analysis

Liang Liao; Xu Shen; Yanning Zhang

doi:10.1109/FSKD.2011.6019571

Image segmentation based on fast kernelized fuzzy clustering analysis

Liang Liao, Xu Shen, Yanning Zhang

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Based on kernelized fuzzy clustering analysis, this paper presents a fast image segmentation algorithm using a speeding-up scheme called reduced set representation. The proposed clustering algorithm has lower computational complexity and could be regarded as the generalized version of the traditional KFCM-I and KFCM-II algorithms. Moreover, an image intensity correction is employed during image segmentation process. With another speeding-up scheme called pre-classification, the proposed intensity correction could further acclerate image segmentation. Experiments of MRI image segmentation have shown the effectiveness of the proposed algorithm, which outperforms in its rivals.

Original language	English
Title of host publication	Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011
Pages	438-442
Number of pages	5
DOIs	https://doi.org/10.1109/FSKD.2011.6019571
State	Published - 2011
Event	2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11 - Shanghai, China Duration: 26 Jul 2011 → 28 Jul 2011

Publication series

Name	Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011
Volume	1

Conference

Conference	2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11
Country/Territory	China
City	Shanghai
Period	26/07/11 → 28/07/11

Keywords

Image intensity correction
Image segmentation
Kernelized clustering
Speed-up scheme

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10.1109/FSKD.2011.6019571

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Liao, L., Shen, X., & Zhang, Y. (2011). Image segmentation based on fast kernelized fuzzy clustering analysis. In Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 (pp. 438-442). Article 6019571 (Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011; Vol. 1). https://doi.org/10.1109/FSKD.2011.6019571

@inproceedings{44c8464079264857b96cd0e61796b32f,

title = "Image segmentation based on fast kernelized fuzzy clustering analysis",

abstract = "Based on kernelized fuzzy clustering analysis, this paper presents a fast image segmentation algorithm using a speeding-up scheme called reduced set representation. The proposed clustering algorithm has lower computational complexity and could be regarded as the generalized version of the traditional KFCM-I and KFCM-II algorithms. Moreover, an image intensity correction is employed during image segmentation process. With another speeding-up scheme called pre-classification, the proposed intensity correction could further acclerate image segmentation. Experiments of MRI image segmentation have shown the effectiveness of the proposed algorithm, which outperforms in its rivals.",

keywords = "Image intensity correction, Image segmentation, Kernelized clustering, Speed-up scheme",

author = "Liang Liao and Xu Shen and Yanning Zhang",

year = "2011",

doi = "10.1109/FSKD.2011.6019571",

language = "英语",

isbn = "9781612841816",

series = "Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011",

pages = "438--442",

booktitle = "Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011",

note = "2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11 ; Conference date: 26-07-2011 Through 28-07-2011",

}

Liao, L, Shen, X & Zhang, Y 2011, Image segmentation based on fast kernelized fuzzy clustering analysis. in Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011., 6019571, Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, vol. 1, pp. 438-442, 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11, Shanghai, China, 26/07/11. https://doi.org/10.1109/FSKD.2011.6019571

Image segmentation based on fast kernelized fuzzy clustering analysis. / Liao, Liang; Shen, Xu; Zhang, Yanning.
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011. 2011. p. 438-442 6019571 (Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Shen, Xu

AU - Zhang, Yanning

PY - 2011

Y1 - 2011

N2 - Based on kernelized fuzzy clustering analysis, this paper presents a fast image segmentation algorithm using a speeding-up scheme called reduced set representation. The proposed clustering algorithm has lower computational complexity and could be regarded as the generalized version of the traditional KFCM-I and KFCM-II algorithms. Moreover, an image intensity correction is employed during image segmentation process. With another speeding-up scheme called pre-classification, the proposed intensity correction could further acclerate image segmentation. Experiments of MRI image segmentation have shown the effectiveness of the proposed algorithm, which outperforms in its rivals.

AB - Based on kernelized fuzzy clustering analysis, this paper presents a fast image segmentation algorithm using a speeding-up scheme called reduced set representation. The proposed clustering algorithm has lower computational complexity and could be regarded as the generalized version of the traditional KFCM-I and KFCM-II algorithms. Moreover, an image intensity correction is employed during image segmentation process. With another speeding-up scheme called pre-classification, the proposed intensity correction could further acclerate image segmentation. Experiments of MRI image segmentation have shown the effectiveness of the proposed algorithm, which outperforms in its rivals.

KW - Image intensity correction

KW - Image segmentation

KW - Kernelized clustering

KW - Speed-up scheme

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M3 - 会议稿件

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SN - 9781612841816

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BT - Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011

T2 - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

Y2 - 26 July 2011 through 28 July 2011

ER -

Image segmentation based on fast kernelized fuzzy clustering analysis

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