Description

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. Our method outperforms recent approaches for A/V classification. Normal retinal images vessels are segment using the morphological operations and then using graph trace algorithm for identification the center line of the vessels and trace the pixel values as a feature and use the KNN classifier to classify the feature and assign which is the artery and which is the vein in retinal image. In feature we extract the thickness of the vessels to identify the disease details.