Using Retinex Image Enhancement to Improve the Artery/Vein Classification in Retinal Images

A precise characterization of the retinal vessels into veins and arteries is necessary to develop automatic tools for diagnosis support. As medical experts, most of the existing methods use the vessel lightness or color for the classification, since veins are darker than arteries. However, retinal images often suffer from inhomogeneity problems in lightness and contrast, mainly due to the image capturing process and the curved retina surface. This fact and the similarity between both types of vessels make difficult an accurate classification, even for medical experts. In this paper, we propose an automatic approach for the retinal vessel classification that combines an image enhancement procedure based on the retinex theory and a clustering process performed in several overlapped areas within the retinal image. Experimental results prove the accuracy of our approach in terms of miss-classified and unclassified vessels.