Optic cup segmentation for glaucoma detection using low-rank superpixel representation

Yanwu Xu; Lixin Duan; Stephen Lin; Xiangyu Chen; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

doi:10.1007/978-3-319-10404-1_98

Optic cup segmentation for glaucoma detection using low-rank superpixel representation

Yanwu Xu, Lixin Duan, Stephen Lin, Xiangyu Chen, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu

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

63 Citations (Scopus)

Abstract

We present an unsupervised approach to segment optic cups in fundus images for glaucoma detection without using any additional training images. Our approach follows the superpixel framework and domain prior recently proposed in [1], where the superpixel classification task is formulated as a low-rank representation (LRR) problem with an efficient closed-form solution. Moreover, we also develop an adaptive strategy for automatically choosing the only parameter in LRR and obtaining the final result for each image. Evaluated on the popular ORIGA dataset, the results show that our approach achieves better performance compared with existing techniques.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
Publisher	Springer Verlag
Pages	788-795
Number of pages	8
Edition	PART 1
ISBN (Print)	9783319104034
DOIs	https://doi.org/10.1007/978-3-319-10404-1_98
Publication status	Published - 2014
Externally published	Yes
Event	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States Duration: 14 Sept 2014 → 18 Sept 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8673 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Country/Territory	United States
City	Boston, MA
Period	14/09/14 → 18/09/14

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-10404-1_98

Cite this

Xu, Y., Duan, L., Lin, S., Chen, X., Wong, D. W. K., Wong, T. Y., & Liu, J. (2014). Optic cup segmentation for glaucoma detection using low-rank superpixel representation. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings (PART 1 ed., pp. 788-795). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8673 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-319-10404-1_98

Xu, Yanwu ; Duan, Lixin ; Lin, Stephen et al. / Optic cup segmentation for glaucoma detection using low-rank superpixel representation. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2014. pp. 788-795 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Optic cup segmentation for glaucoma detection using low-rank superpixel representation",

abstract = "We present an unsupervised approach to segment optic cups in fundus images for glaucoma detection without using any additional training images. Our approach follows the superpixel framework and domain prior recently proposed in [1], where the superpixel classification task is formulated as a low-rank representation (LRR) problem with an efficient closed-form solution. Moreover, we also develop an adaptive strategy for automatically choosing the only parameter in LRR and obtaining the final result for each image. Evaluated on the popular ORIGA dataset, the results show that our approach achieves better performance compared with existing techniques.",

author = "Yanwu Xu and Lixin Duan and Stephen Lin and Xiangyu Chen and Wong, {Damon Wing Kee} and Wong, {Tien Yin} and Jiang Liu",

year = "2014",

doi = "10.1007/978-3-319-10404-1_98",

language = "English",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings",

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Xu, Y, Duan, L, Lin, S, Chen, X, Wong, DWK, Wong, TY & Liu, J 2014, Optic cup segmentation for glaucoma detection using low-rank superpixel representation. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8673 LNCS, Springer Verlag, pp. 788-795, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, Boston, MA, United States, 14/09/14. https://doi.org/10.1007/978-3-319-10404-1_98

Optic cup segmentation for glaucoma detection using low-rank superpixel representation. / Xu, Yanwu; Duan, Lixin; Lin, Stephen et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2014. p. 788-795 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8673 LNCS, No. PART 1).

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

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AU - Chen, Xiangyu

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AU - Wong, Tien Yin

AU - Liu, Jiang

PY - 2014

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AB - We present an unsupervised approach to segment optic cups in fundus images for glaucoma detection without using any additional training images. Our approach follows the superpixel framework and domain prior recently proposed in [1], where the superpixel classification task is formulated as a low-rank representation (LRR) problem with an efficient closed-form solution. Moreover, we also develop an adaptive strategy for automatically choosing the only parameter in LRR and obtaining the final result for each image. Evaluated on the popular ORIGA dataset, the results show that our approach achieves better performance compared with existing techniques.

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Xu Y, Duan L, Lin S, Chen X, Wong DWK, Wong TY et al. Optic cup segmentation for glaucoma detection using low-rank superpixel representation. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1 ed. Springer Verlag. 2014. p. 788-795. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-319-10404-1_98

Optic cup segmentation for glaucoma detection using low-rank superpixel representation

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