Premium
Computer‐aided diagnosis based on enhancement of degraded fundus photographs
Author(s) -
Jin Kai,
Zhou Mei,
Wang Shaoze,
Lou Lixia,
Xu Yufeng,
Ye Juan,
Qian Dahong
Publication year - 2018
Publication title -
acta ophthalmologica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.534
H-Index - 87
eISSN - 1755-3768
pISSN - 1755-375X
DOI - 10.1111/aos.13573
Subject(s) - fundus (uterus) , optometry , computer science , computer vision , artificial intelligence , ophthalmology , medicine , computer graphics (images)
Purpose Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. Methods A new method is used to enhance degraded‐quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast‐limited adaptive histogram equalization. Human visual system (HVS)‐based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. Results The study included 191 degraded‐quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0–1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age‐related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. Conclusion The relatively simple method for enhancing degraded‐quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS‐based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer‐aided diagnosis.