Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Abstract

Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.