Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation

Dante Mújica-Vargas; Francisco Javier Gallegos-Funes; Alberto J. Rosales-Silva; Rene Cruz-Santiago

doi:10.1007/978-3-642-25330-0_42

Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation

Dante Mújica-Vargas, Francisco Javier Gallegos-Funes, Alberto J. Rosales-Silva, Rene Cruz-Santiago

Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Zacatenco

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

1 Scopus citations

Abstract

Image segmentation is a key step for many images analysis applications. So far, there does not exist a general method to segment suitable all images, regardless if these are corrupted or noise free. In this paper, we propose to modify the Fuzzy C-means clustering algorithm and the FCM-S1 variant by using the RML-estimator. The idea to our method is to get robust clustering algorithms able to segment images with different type and levels of noises. The performance of the proposed algorithms is tested on synthetic and real images. Experimental results show that the proposed algorithms are more robust to the noise presence and more effective than the comparative algorithms.

Original language	English
Title of host publication	Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings
Pages	474-486
Number of pages	13
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-25330-0_42
State	Published - 2011
Event	10th Mexican International Conference on Artificial Intelligence, MICAI 2011 - Puebla, Mexico Duration: 26 Nov 2011 → 4 Dec 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7095 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th Mexican International Conference on Artificial Intelligence, MICAI 2011
Country/Territory	Mexico
City	Puebla
Period	26/11/11 → 4/12/11

Keywords

Fuzzy C-Means
RML-estimator
noise
robust estimators
segmentation

Access to Document

10.1007/978-3-642-25330-0_42

Cite this

Mújica-Vargas, D., Gallegos-Funes, F. J., Rosales-Silva, A. J., & Cruz-Santiago, R. (2011). Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation. In Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings (PART 2 ed., pp. 474-486). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7095 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-25330-0_42

Mújica-Vargas, Dante ; Gallegos-Funes, Francisco Javier ; Rosales-Silva, Alberto J. et al. / Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation. Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2. ed. 2011. pp. 474-486 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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Mújica-Vargas, D, Gallegos-Funes, FJ , Rosales-Silva, AJ & Cruz-Santiago, R 2011, Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation. in Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7095 LNAI, pp. 474-486, 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, 26/11/11. https://doi.org/10.1007/978-3-642-25330-0_42

Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation. / Mújica-Vargas, Dante; Gallegos-Funes, Francisco Javier ; Rosales-Silva, Alberto J. et al.
Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2. ed. 2011. p. 474-486 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7095 LNAI, No. PART 2).

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

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AB - Image segmentation is a key step for many images analysis applications. So far, there does not exist a general method to segment suitable all images, regardless if these are corrupted or noise free. In this paper, we propose to modify the Fuzzy C-means clustering algorithm and the FCM-S1 variant by using the RML-estimator. The idea to our method is to get robust clustering algorithms able to segment images with different type and levels of noises. The performance of the proposed algorithms is tested on synthetic and real images. Experimental results show that the proposed algorithms are more robust to the noise presence and more effective than the comparative algorithms.

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Mújica-Vargas D, Gallegos-Funes FJ , Rosales-Silva AJ, Cruz-Santiago R. Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation. In Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2 ed. 2011. p. 474-486. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-25330-0_42

Robust RML estimator - Fuzzy C-means clustering algorithms for noisy image segmentation

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