A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina

TLDR

The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. The study presents a computer‑assisted algorithm for detecting exudates, a key diagnostic task for diabetic retinopathy. The algorithm locates exudates by exploiting high grey‑level variation and morphological reconstruction, detects the optic disc with morphological filtering and watershed, and was evaluated on a small image database against a human grader, with robustness to parameter changes assessed. As a result, we obtain a mean sensitivity of 92.8 % and a mean predictive value of 92.4 %.

Abstract

In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. Hence, detection of exudates is an important diagnostic task, in which computer assistance may play a major role. Exudates are found using their high grey level variation, and their contours are determined by means of morphological reconstruction techniques. The detection of the optic disc is indispensable for this approach. We detect the optic disc by means of morphological filtering techniques and the watershed transformation. The algorithm has been tested on a small image data base and compared with the performance of a human grader. As a result, we obtain a mean sensitivity of 92.8% and a mean predictive value of 92.4%. Robustness with respect to changes of the parameters of the algorithm has been evaluated.