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

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.

• Autores: Pérez, Andrés D.; Perdomo, Oscar; Rios, Hernán; Rodríguez, Francisco; González, Fabio A.
• Tipo de recurso: Part of book
• Fecha de publicación: 2020
• Institución: Escuela Colombiana de Ingeniería Julio Garavito
• Repositorio: Repositorio Institucional ECI
• Idioma: eng
• OAI Identifier: oai:repositorio.escuelaing.edu.co:001/1487
• Acceso en línea: https://repositorio.escuelaing.edu.co/handle/001/1487; https://doi.org/10.1007/978-3-030-63419-3_19
• Palabra clave: Fundus of the eye - Diagnosis; Fondo del ojo - Diagnóstico; Diagnóstico por imagen; Diagnostic imaging; Image quality enhancement; Synthetic quality degradation; Eye fundus image; Conditional generative adversarial network; Mejora de la calidad de la imagen; Degradación sintética de la calidad; Imagen del fondo del ojo; Red adversarial generativa condicional
• Rights: closedAccess
• License: http://purl.org/coar/access_right/c_14cb