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

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaCapítulo (revisado por pares)revisión exhaustiva

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 originalInglés estadounidense
Título de la publicación alojadaOphthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditoresHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas185-194
Número de páginas10
Volumen12069
ISBN (versión digital)978-3-030-63419-3
ISBN (versión impresa)978-3-030-63418-6
DOI
EstadoPublicada - nov 20 2020
Evento6th 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 2020oct 8 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12069 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia6th 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/TerritorioPerú
CiudadLima
Período10/8/2010/8/20

All Science Journal Classification (ASJC) codes

  • Ciencia computacional teórica
  • Informática (todo)

Huella

Profundice en los temas de investigación de 'A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement'. En conjunto forman una huella única.

Citar esto