An Automatic Graph Based Approach For Artery/Vein Classification in Retinal Image

Abstract

The classification of retinal vessels into artery/vein.(A/V) is an important phase for automating
the detection of vascular changes, and for the calculation of characteristic signs associated with several
systemic diseases such as diabetes , hypertension , and other Cardiovascular conditions. This paper presents
an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal
vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection
point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of
a vessel segment as A/V is performed through the combination of the graph- based labeling results with a set
of intensity features.
The results of this proposed method are compared with manual labeling for three public databases.
Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and
VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for
A/V classification