Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal
blood vessel detection, vascular network topology estimation, and arteries / veins classi cation
are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide
spectrum of diseases.
Methods: We propose a new framework for precisely segmenting retinal vasculatures,
constructing retinal vascular network topology, and separating the arteries and veins. A
non-local total variation inspired Retinex model is employed to remove the image intensity
inhomogeneities and relatively poor contrast. For better generalizability and segmentation
performance, a superpixel based line operator is proposed as to distinguish between lines and
the edges, thus allowing more tolerance in the position of the respective contours. The concept
of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel
network into arteries and veins.
Results: The proposed segmentation method yields competitive results on three pub-
lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com-
pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964,
respectively. The topology estimation approach has been applied to ve public databases
1
(DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830,
0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation
based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and
VICAVR) are 0.90.9, 0.910, and 0.907, respectively.
Conclusions: The experimental results show that the proposed framework has e ectively
addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon-
struction. The vascular topology information signi cantly improves the accuracy on arteries
/ veins classi cation
