Retinal Image Analysis Oriented to the Clinical Task

Ophthalmology can profit greatly from the analysis of digital images because they can aid in establishing an early diagnosis even before the first symptoms appear. This dissertation [1] contributes to the digital analysis of such 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 [2], poor image quality [3], automated focusing [4], image segmentation [5], change detection [6], space-invariant (SI) [6] and space-variant (SV) [7] blind deconvolution (BD). Digital retinal image analysis can be effective and cost-efficient for disease management, computer-aided diagnosis, screening and telemedicine and applicable to a variety of disorders such as glaucoma, macular degeneration, and retinopathy [8, 9].