Automated Exudates Detection and Grading of Diabetic Maculopathy in Digital Retinal Images

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type1 diabetes and over 60% of patients with type2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. In this work, we present a computer-based intelligent system based on mathematical morphology for detecting exudates in color eye fundus images and identification of clinically significant, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images using morphological image processing techniques, which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.