The aim of this study is about tracing filamentary structures in both neuronal and retinal images. It is often crucial to identify single neurons in neuronal networks, or separate vessel tree structures in retinal blood vessel networks, in applications such as drug screening for neurological disorders or computeraided diagnosis of diabetic retinopathy. Both tasks are challenging as the same bottleneck issue of filament crossovers is commonly encountered, which essentially hinders the ability of existing systems to conduct large-scale drug screening or practical clinical usage.
A retinal vessel tracking method based on Bayesian theory and multi-scale line detection is proposed in this paper. The optic disk is located by a PCA method and the initial points of tracking are identified. In each step, candidate points for vessel edges are selected on a semi-ellipse. Three types of vessel structure are considered in the tracking: normal vessel, branching, and crossing. To determine the new pair of edge points, the characteristics of the vessel intensity profiles along both the cross section and the longitudinal direction are considered in the tracking.
As an early indication of diseases including diabetes, hypertension,and retinopathy of prematurity, structural study of retinal vessels becomes increasingly important. These studies have driven the