Automatic Diabetic Retinopathy Detection Using Digital Image Processing

Diabetic retinopathy (DR) is one of the most common reasons for blindness in the working-age population of world. Diabetic Retinopathy is an eye disease, which occurs with long-standing untreated diabetes. Progression to vision impairment can be slowed down or stopped if DR is detected on time; In detection or screening of DR, automatic methods can play an important role. In this paper, we proposed a novel method to detect hard exudates with high accuracy with respect to lesion level. In the present method we initially detected the possible candidate exudate lesions by using the back ground subtraction methodology. Following the subsequent steps, in the last stage of algorithm we removed the false exudate lesion detections using the de-correlation stretch based method. We tested our algorithm on publicly available DiaretDB database, which contains the ground truth for all images. We achieved high performance results such as sensitivity of 0.87 and F-Score of 0.78 and Positive Predict Value (PPV) of 0.76 for hard exudate lesion level detection, compared to the existing state of art techniques.