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Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACC i ), 91.07% per-pixel positive predictive value (PPV p ), and 85.25% per-pixel sensitivity (SE p ) were reached for the detection of RLs. Using the public database, 90.16% ACC i , 96.26% PPV_ p , and 84.79% SE p were obtained. As for the detection of EXs, 95.41% ACC i , 96.01% PPV_ p , and 89.42% SE_ p were reached with the proprietary database. Using the public database, 91.80% ACC i , 98.59% PPV p , and 91.65% SE p were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy