Measurements of retinal blood vessel using morphology have been shown to be related to the risk of cardiovascular diseases. The improper identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a post-processing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment patterns. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 50 retinal images. The patterns are stored in the database. We use the stored patterns to perform authentication process.