Effective features for artery-vein classification in digital fundus images

In this paper we present an analysis of image features used to discriminate arteries and veins in digital fundus images. Methods proposed in the literature to analyze the vasculature of the retina and compute diagnostic indicators like the Arteriolar to Venular ratio (AVR), use, in fact, different approaches for this classification task, extracting different color features and exploiting different additional information. We concentrate our analysis on finding optimal features for the vessel classification, considering not only simple color features, but also spatial location and vessel size and testing different supervised labeling approaches. The results obtained show that best results are obtained mixing features related with color values and contrast inside and outside the vessels and positional information. Furthermore, the discriminative power of the features changes with the image resolution and best results are not obtained at the finest one. Our experiments demonstrate that using a good set of descriptors it is possible to achieve very good classification performances even without using vascular connectivity information.