Microaneurysms detection using a novel neighborhood analysis

The earliest sign of the diabetic retinopathy is the appearance of small red dots in retinal fundus images, designated by microaneurysms. In this paper a scale-space based method is proposed for the microaneurysms detection. Initially, the method performs a segmentation of the retinal vasculature and defines a global set of microaneurysms candidates, using both coarser and finer scales. Using the finer scales, a set of microaneurysms candidates are analysed in terms of shape and size. Then, a set of gaussian-shaped matched filters are used to reduce the number of false microaneurysms candidates. Each candidate is labeled as a true microaneurysm using a new neighborhood analysis method. The proposed algorithm was tested with the training Retinopathy Online Challenge (ROC) dataset, revealing a 47% Sensitivity with an average number of 37.9 false positives per image.