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

Producción científica: Capítulo en Libro/Informe › Capítulo (revisado por pares) › revisión exhaustiva

15 Citas (Scopus)

Resumen

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.

Idioma original	Inglés estadounidense
Título de la publicación alojada	Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editores	Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	185-194
Número de páginas	10
Volumen	12069
ISBN (versión digital)	978-3-030-63419-3
ISBN (versión impresa)	978-3-030-63418-6
DOI	https://doi.org/10.1007/978-3-030-63419-3_19
Estado	Publicada - nov. 20 2020
Evento	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, Perú Duración: oct. 8 2020 → oct. 8 2020

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	12069 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	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
País/Territorio	Perú
Ciudad	Lima
Período	10/8/20 → 10/8/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

Ciencia computacional teórica
Ciencia de la Computación General

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

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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. En 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.",

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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. En 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, Perú, 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).

Producción científica: Capítulo en Libro/Informe › Capítulo (revisado por pares) › revisión exhaustiva

TY - CHAP

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

A2 - Garvin, Mona K.

A2 - MacGillivray, Tom

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Pérez AD, Perdomo O, Rios H, Rodríguez F, González FA. A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. En Fu H, Garvin MK, MacGillivray T, Xu Y, Zheng Y, editores, 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

Resumen

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ODS de las Naciones Unidas

Áreas temáticas de ASJC Scopus

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