Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression

Yanwu Xu; Xinting Gao; Stephen Lin; Damon Wing Kee Wong; Jiang Liu; Dong Xu; Ching Yu Cheng; Carol Y. Cheung; Tien Yin Wong

doi:10.1007/978-3-642-40763-5_58

Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression

Yanwu Xu
, Xinting Gao
, Stephen Lin
, Damon Wing Kee Wong
, Jiang Liu
, Dong Xu
, Ching Yu Cheng
, Carol Y. Cheung
, Tien Yin Wong

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

69 Citations (Scopus)

Abstract

Cataracts, which result from lens opacification, are the leading cause of blindness worldwide. Current methods for determining the severity of cataracts are based on manual assessments that may be weakened by subjectivity. In this work, we propose a system to automatically grade the severity of nuclear cataracts from slit-lamp images. We introduce a new feature for cataract grading together with a group sparsity-based constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. In experiments on a large database of 5378 images, our system outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (ε) of 0.336, a 69.0% exact integral agreement ratio (R₀), a 85.2% decimal grading error ≤ 0.5 (R_e0.5), and a 98.9% decimal grading error ≤ 1.0 (R_e1.0). Through a more objective grading of cataracts using our proposed system, there is potential for better clinical management of the disease.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	468-475
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-40763-5_58
Publication status	Published - 2013
Externally published	Yes
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sept 2013 → 26 Sept 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8150 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 26/09/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-40763-5_58

Cite this

Xu, Y., Gao, X., Lin, S., Wong, D. W. K., Liu, J., Xu, D., Cheng, C. Y., Cheung, C. Y., & Wong, T. Y. (2013). Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 2 ed., pp. 468-475). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40763-5_58

Xu, Yanwu ; Gao, Xinting ; Lin, Stephen et al. / Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2. ed. 2013. pp. 468-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{4efbd6b6d42a450ba713ef0f081e6534,

title = "Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression",

abstract = "Cataracts, which result from lens opacification, are the leading cause of blindness worldwide. Current methods for determining the severity of cataracts are based on manual assessments that may be weakened by subjectivity. In this work, we propose a system to automatically grade the severity of nuclear cataracts from slit-lamp images. We introduce a new feature for cataract grading together with a group sparsity-based constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. In experiments on a large database of 5378 images, our system outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (ε) of 0.336, a 69.0\% exact integral agreement ratio (R0), a 85.2\% decimal grading error ≤ 0.5 (Re0.5), and a 98.9\% decimal grading error ≤ 1.0 (Re1.0). Through a more objective grading of cataracts using our proposed system, there is potential for better clinical management of the disease.",

author = "Yanwu Xu and Xinting Gao and Stephen Lin and Wong, \{Damon Wing Kee\} and Jiang Liu and Dong Xu and Cheng, \{Ching Yu\} and Cheung, \{Carol Y.\} and Wong, \{Tien Yin\}",

year = "2013",

doi = "10.1007/978-3-642-40763-5\_58",

language = "English",

isbn = "9783642407628",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings",

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note = "16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",

}

Xu, Y, Gao, X, Lin, S, Wong, DWK, Liu, J, Xu, D, Cheng, CY, Cheung, CY & Wong, TY 2013, Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, 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. 8150 LNCS, pp. 468-475, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-40763-5_58

Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. / Xu, Yanwu; Gao, Xinting; Lin, Stephen et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2. ed. 2013. p. 468-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2).

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

TY - GEN

T1 - Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression

AU - Xu, Yanwu

AU - Gao, Xinting

AU - Lin, Stephen

AU - Wong, Damon Wing Kee

AU - Liu, Jiang

AU - Xu, Dong

AU - Cheng, Ching Yu

AU - Cheung, Carol Y.

AU - Wong, Tien Yin

PY - 2013

Y1 - 2013

N2 - Cataracts, which result from lens opacification, are the leading cause of blindness worldwide. Current methods for determining the severity of cataracts are based on manual assessments that may be weakened by subjectivity. In this work, we propose a system to automatically grade the severity of nuclear cataracts from slit-lamp images. We introduce a new feature for cataract grading together with a group sparsity-based constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. In experiments on a large database of 5378 images, our system outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (ε) of 0.336, a 69.0% exact integral agreement ratio (R0), a 85.2% decimal grading error ≤ 0.5 (Re0.5), and a 98.9% decimal grading error ≤ 1.0 (Re1.0). Through a more objective grading of cataracts using our proposed system, there is potential for better clinical management of the disease.

AB - Cataracts, which result from lens opacification, are the leading cause of blindness worldwide. Current methods for determining the severity of cataracts are based on manual assessments that may be weakened by subjectivity. In this work, we propose a system to automatically grade the severity of nuclear cataracts from slit-lamp images. We introduce a new feature for cataract grading together with a group sparsity-based constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. In experiments on a large database of 5378 images, our system outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (ε) of 0.336, a 69.0% exact integral agreement ratio (R0), a 85.2% decimal grading error ≤ 0.5 (Re0.5), and a 98.9% decimal grading error ≤ 1.0 (Re1.0). Through a more objective grading of cataracts using our proposed system, there is potential for better clinical management of the disease.

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U2 - 10.1007/978-3-642-40763-5_58

DO - 10.1007/978-3-642-40763-5_58

M3 - Conference contribution

C2 - 24579174

AN - SCOPUS:84885902033

SN - 9783642407628

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 468

EP - 475

BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings

T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013

Y2 - 22 September 2013 through 26 September 2013

ER -

Xu Y, Gao X, Lin S, Wong DWK, Liu J, Xu D et al. Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2 ed. 2013. p. 468-475. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-40763-5_58

Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression

Abstract

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UN SDGs

ASJC Scopus subject areas

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