In biomedical imaging analysis and computer-assisted diagnosis, segmentation analysis is an intense field of research and development. The most difficult part of medical image analysis is the automated localization and delineation of structures of interest. Automated data evaluation is one way of enhancing the clinical utility of measurements. In particular, medical image segmentation extracts meaningful information and facilitates the display of this information in a clinically relevant way. Segmentation of blood vessels in retinal images allows early diagnosis of disease; automating this process provides several benefits including minimizing subjectivity and eliminating a painstaking, tedious task. This paper, addresses the problem of automatically identifying true vessels as a post processing step to vascular structure segmentation. The segmented vascular structure is modeled as a vessel segment graph and formulates the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. A method is designed to solve this optimization problem and show that the proposed approach is able to achieve good pixel precision and recalls all true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.