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Identification and measurement of blood vessels in retinal images could allow quantitative evaluation of clinical features, which may allow early diagnosis and effective monitoring of therapies in retinopathy. A new system is proposed for the automatic extraction of the vascular structure in retinal images, based on a sparse tracking technique. After processing pixels on a grid of rows and columns to determine a set of starting points (seeds), the tracking procedure starts. It moves along the vessel by analyzing subsequent vessel cross sections (lines perpendicular to the vessel direction), and extracting the vessel center, calibre and direction. Vessel points in a cross section are found by means of a fuzzy c-means classifier. When tracking stops because of a critical area, e.g. low contrast, bifurcation or crossing, a "bubble technique" module is run. It grows and analyzes circular scan lines around the critical points, allowing the exploration of the vessel structure beyond the critical areas. After tracking the vessels, identified segments are connected by a greedy connection algorithm. Finally bifurcations and crossings are identified analyzing vessel end points with respect to the vessel structure. Numerical evaluation of the performances of the system compared to human expert are reported.

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