Automated detection of diabetic retinopathy in blurred digital fundus images

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). In this study, we propose a system for automated classification of normal, and abnormal retinal images by automatically detecting the blood vessels, hard exudates microaneurysms, entropy and homogeneity. The objective measurements such as blood vessels area, exudates area, microaneurysms area, entropy and homogeneity are computed from the processed retinal images. These objective measurements are finally fed to the artificial neural network (ANN) classifier for the automatic classification. Different approaches for image restoration are tested and compared on Fundus images. The effect of restoration on the automatic detection process is investigated in this paper.