Construction of retinal vascular trees via curvature orientation prior

Constructing vascular trees is a prerequisite for graph-based methods of arterial/venous classification, vessel abnormities measurement and various diseases evaluation. Previous works mainly focused on geometrical and topological properties of vessel segments and applied fixed rules or constraint optimization to building vessel trees. In this paper, we propose a novel vascular tree generation method with vessel curvature orientation as a prior. We firstly build a rough graph from vessel centerline image and then modify graph misrepresentations (false edge and missing edge) based on vessel landmarks, which can be extracted from multi-scale curvature orientation histogram. Next we separate different trees at vessel junctions by curvature orientation clustering. Two graph-based applications, vessel classification and vessel diameter ratio measurement, are tested on two different image sets to validate our vascular tree construction approach. Experiments demonstrate that our approach generates a more reliable vascular graph and achieves a comparatively high vessel classification accuracy of 83.21% and 85.06% within entire image in RITE and INSPIRE database respectively.