Microaneurysm (MA) detection via sparse representation classifier with MA and non-MA dictionary learning

Diabetic retinopathy (DR) is a common complication of diabetes that damages the retina and leads to sight loss if treated late. In its earliest stage, DR can be diagnosed by microaneurysm (MA). Although some algorithms have been developed, the accurate detection of MA in color retinal images is still a challenging problem. In this paper we propose a new method to detect MA based on Sparse Representation Classifier (SRC). We first roughly locate MA candidates by using multi-scale Gaussian correlation filtering, and then classify these candidates with SRC. Particularly, two dictionaries, one for MA and one for non-MA, are learned from example MA and non-MA structures, and are used in the SRC process. Experimental results on the ROC database show that the proposed method can well distinguish MA from non-MA objects.