Automated feature extraction for early detection of diabetic retinopathy in fundus images

Automated detection of lesions in retinal images can assist in early diagnosis and screening of a common disease: Diabetic Retinopathy. A robust and computationally efficient approach for the localization of the different features and lesions in a fundus retinal image is presented in this paper. Since many features have common intensity properties, geometric features and correlations are used to distinguish between them. We propose a new constraint for optic disk detection where we first detect the major blood vessels and use the intersection of these to find the approximate location of the optic disk. This is further localized using color properties. We also show that many of the features such as the blood vessels, exudates and microaneurysms and hemorrhages can be detected quite accurately using different morphological operations applied appropriately. Extensive evaluation of the algorithm on a database of 516 images with varied contrast, illumination and disease stages yields 97.1% success rate for optic disk localization, a sensitivity and specificity of 95.7%and 94.2%respectively for exudate detection and 95.1% and 90.5% for microaneurysm/hemorrhage detection. These compare very favorably with existing systems and promise real deployment of these systems.