Automatic hemorrhage detection in color fundus images based on gradual removal of vascular branches

Hemorrhages in color fundus images usually vary in size and shape, and some are even connected with the retinal vasculature such that they are often omitted by previous detection methods. In this paper, we propose a new method to deal with these problems. During the hemorrhage candidate extraction stage, dark regions and retinal vasculature are segmented out respectively. Their individual advantages are fused together into a binary image with good vascular continuity. Then the gradual removal of vascular branches is applied to generate hemorrhage candidates. In the classification stage, a SVM classifier using 49 features is trained to classify the candidates into hemorrhages and non-hemorrhages. The experiments on DIARETDB1 and DIARETDB0 show that our method achieves good performance on sensitivity and specificity, especially at lesion level.