Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density

Due to its blood microcirculation, the retina is one of the first organs affected by hypertension and diabetes: retinal damages can lead to serious visual loss, that can be avoided by an early diagnosis. The most distinctive sign of diabetic retinopathy or severe hypertensive retinopathy are dark lesions such as haemorrhages and microaneurysms (HM), and bright lesions such as hard exudates (HE) and cotton wool spots (CWS). Automatic detection of their presence in the retina is thus of paramount importance for assessing the presence of retinopathy, and therefore relieve the burden of image examination by retinal experts. The most widespread scheme for automatically detect retinal lesion rely on a initial segmentation and a subsequent refinement stage, usually by means of a supervised classification or based on heuristic rules. The first step is therefore required not to lose any possible lesions, at the same time discarding as much of the normal retina as possible. In this work we propose a simple and effective method to detect and identify haemorrhagic (dark) lesions in retinal images, by using a simple local thresholding followed by an evaluation of a measure of the spatial density of the pixels selected at the first step. We evaluate the algorithm on 6 images presenting dark lesions extracted from a database of 60 annotated images, resulting in a mean detection rate of 94% the lesions present in an image, with good performance in term of false candidate rejection.