Fast adaptive axis-based segmentation of retinal vessels through matched filters

Ocular fundus images provide important information about retinal degeneration, which may be related to acute pathologies or to early signs of systemic diseases. An automatic and quantitative assessment of vessel morphological features, such as diameters and tortuosity, can improve clinical diagnosis and evaluation of retinopathy. We propose a new method to accurately evaluate vessel diameters and centerline starting from an estimated network of vessel axes. The algorithm extracts points laying on the vessel borders by means of an efficient mono-dimensional matched filtering approach. The orientation of the filter kernel is chosen according to the information provided by the network and the appropriate scale is computed by means of an initial diameter estimation performed on the vessels cross section profiles before the filtering process. An adaptive correction step is then run to fix non consistent diameters, in order to obtain a regular and continuous vessel morphology. Vessel border refinement finally yields an accurate representation of the vascular structure. Average calibers were evaluated, for a set of 739 vessel segments, both manually by an expert and automatically by the proposed method and results show high correlation (ρ = 0.97).