A manually-labeled, artery/vein classified benchmark for the DRIVE dataset

Abstract

The classification of retinal vessels into arteries and veins is an important step for the analysis of retinal vascular trees, for which the scientists have proposed several classification methods. An obvious concern regarding the strength of these methodologies is the closeness of the result of a particular method to the gold standard. Unfortunately, the research community lacks benchmarks, resulting in increased subjective error, biased opinion and an uncertain progress. This paper introduces a manually-labeled, artery/vein categorized gold standard image database, as an extension of the most widely used image set DRIVE. The labeling criterion is set after a careful analysis of the physiological facts about the retinal vascular system. In addition, the labeling process also includes several versions of original images to get certainty. A two-step validation phase consists of verification from the trained computer vision observers and a professional ophthalmologist, followed by a comparison with a gold standard set for the junction locations introduced in V4-Like filters. Our gold standard is in highly reliable form; offers research community for the result comparison and progress evaluation.