Retinal image analysis: Image processing and feature extraction oriented to the clinical task

Abstract

Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract quantitative information about many diseases. Modern ophthalmology relies on the advances in digital imaging and computing power. In this paper we present an overview of the results from the doctoral dissertation by Andrés G. Marrugo. This dissertation contributes to the digital analysis of retinal images and the problems that arise along the imaging pipeline of fundus photography, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination compensation, poor image quality, automated focusing, image segmentation, change detection, space-invariant (SI) and space-variant (SV) blind deconvolution (BD). Digital retinal image analysis can be effective and cost-efficient for disease management, computeraided diagnosis, screening and telemedicine and applicable to a variety of disorders such as glaucoma, macular degeneration, and retinopathy.