Artery/Vein Discrimination Using Graph Based Approach and LDA Classifier

Analyzing the retinal blood vessels can provide very helpful information to doctors for early detection of diseases Such as diabetic retinopathy due to large number of patients. For this generalized arteriolar narrowing, which is inversely related to higher blood pressure levels is usually expressed by the Arteriolar-to-Venular diameter Ratio (AVR). The AVR value can also be an indicator of other diseases, like hypertension, and other cardiovascular conditions. Among other image processing operations, the estimation of AVR requires vessel segmentation, accurate vessel width measurement, and artery/vein (A/V) classification. 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 labelling results with a set of intensity features. 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.