Retinal artery and vein classification optimization method based on vascular topology

Arteriosclerosis is an independent predictor of cardiovascular disease which is one of the most important causes of human death. Retinal vascular features parameters are important for intelligent diagnosis of arteriosclerosis. These parameters depend on accurate artery and vein (A/V) classification result, but the convolutional neural networks (CNN) used for retinal A/V classification all have some A/V misclassification. In this paper, an optimization method is proposed to optimize the A/V classification of CNN basing on vascular topology. A multi-level lightweight algorithm is proposed to obtain the lightweight information of the vascular skeleton. Using the knowledge of graph theory, the multi-lightweight algorithm preserves the continuous and complete center line structure information. And then, we use multilevel Dijkstra algorithm to estimate the vascular topology on the lightweight vessel skeleton graph, and use the direction algorithm to obtain the vascular directed topology. Lastly, the topology labels algorithm is found on the type of branch nodes to deliver the A/V information along the vascular directed topology to optimize the A/V classification. Experiments clearly show that our optimization method can correct effectively the A/V misclassification.