A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Andrés D. Pérez; Oscar Perdomo; Hernán Rios; Francisco Rodríguez; Fabio A. González

doi:10.1007/978-3-030-63419-3_19

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Andrés D. Pérez, Oscar Perdomo, Hernán Rios, Francisco Rodríguez, Fabio A. González

Research output: Chapter in Book/Inform › Chapter (peer-reviewed) › peer-review

11 Scopus citations

Abstract

Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.

Original language	English (US)
Title of host publication	Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	185-194
Number of pages	10
Volume	12069
ISBN (Electronic)	978-3-030-63419-3
ISBN (Print)	978-3-030-63418-6
DOIs	https://doi.org/10.1007/978-3-030-63419-3_19
State	Published - Nov 20 2020
Event	6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: Oct 8 2020 → Oct 8 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12069 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	10/8/20 → 10/8/20

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

UN SDGs

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

Access to Document

10.1007/978-3-030-63419-3_19

Cite this

Pérez, A. D., Perdomo, O., Rios, H., Rodríguez, F., & González, F. A. (2020). A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. In H. Fu, M. K. Garvin, T. MacGillivray, Y. Xu, & Y. Zheng (Eds.), Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings (Vol. 12069, pp. 185-194). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12069 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63419-3_19

Pérez, Andrés D. ; Perdomo, Oscar ; Rios, Hernán et al. / A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Huazhu Fu ; Mona K. Garvin ; Tom MacGillivray ; Yanwu Xu ; Yalin Zheng. Vol. 12069 Springer Science and Business Media Deutschland GmbH, 2020. pp. 185-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.",

author = "P{\'e}rez, {Andr{\'e}s D.} and Oscar Perdomo and Hern{\'a}n Rios and Francisco Rodr{\'i}guez and Gonz{\'a}lez, {Fabio A.}",

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Pérez, AD, Perdomo, O, Rios, H, Rodríguez, F & González, FA 2020, A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. in H Fu, MK Garvin, T MacGillivray, Y Xu & Y Zheng (eds), Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. vol. 12069, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12069 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 185-194, 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 10/8/20. https://doi.org/10.1007/978-3-030-63419-3_19

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. / Pérez, Andrés D.; Perdomo, Oscar; Rios, Hernán et al.
Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Huazhu Fu; Mona K. Garvin; Tom MacGillivray; Yanwu Xu; Yalin Zheng. Vol. 12069 Springer Science and Business Media Deutschland GmbH, 2020. p. 185-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12069 LNCS).

Research output: Chapter in Book/Inform › Chapter (peer-reviewed) › peer-review

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T1 - A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

AU - Pérez, Andrés D.

AU - Perdomo, Oscar

AU - Rios, Hernán

AU - Rodríguez, Francisco

AU - González, Fabio A.

PY - 2020/11/20

Y1 - 2020/11/20

N2 - Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.

AB - Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.

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

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BT - Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings

A2 - Fu, Huazhu

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A2 - Xu, Yanwu

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Y2 - 8 October 2020 through 8 October 2020

ER -

Pérez AD, Perdomo O, Rios H, Rodríguez F, González FA. A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. In Fu H, Garvin MK, MacGillivray T, Xu Y, Zheng Y, editors, Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. Vol. 12069. Springer Science and Business Media Deutschland GmbH. 2020. p. 185-194. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63419-3_19

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Abstract

Publication series

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All Science Journal Classification (ASJC) codes

UN SDGs

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