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The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44 correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42.

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The morphology of retinal blood vessels contains valuable information for the diagnosis of retinal dysfunctions. The vessels can be segmented from color fundus images but the connectivity of the segmented vessels is not always preserved because of low contrast, imaging noise and artifacts. If a continuous vessel is interpreted as multiple disjoint vessel segments, the morphological measurements such as tortuosity may not be representative of true properties of retinal vessels. We describe an algorithm to identify the vessel segment interruptions based on connected component analysis and then reconnect them using a graph based approach. The proposed method was evaluated on a dataset of 25 vessel segmentation images resulting into a reconnection performance measure of 81.63% compared to the gold standard obtained by the manual reconnection process. Our approach has allowed the complete vessel tree to be connected, and has potential in providing improved morphological measurements.

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