Abstract
Among different retinal analysis tasks, blood vessel extraction plays an important role as it is often the first essential step before any measurement can be made for various applications such as biometric authentication or diagnosis of retinal vascular diseases. In this paper, we present a new method for extraction of blood vessel network with its nodes (bifurcation and crossover points) from retinal images. The first step is to identify pixels with homogeneous vessel elements with a set of four directional filters. Then another step is applied to extract local linear components assuming that a vessel is a set of short linear segments. Through an optimization process this information is combined to extract the vessel network and its nodes. The proposed algorithm was tested on the publicly available DRIVE retinal fundus image database. The experimental results show good precision, recall and F-measure compared to ground truth and a state-of-the-art algorithm for the same dataset.
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Nougrara, Z., Kihal, N., Meunier, J. (2016). Semi-automated Extraction of Retinal Blood Vessel Network with Bifurcation and Crossover Points. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_33
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DOI: https://doi.org/10.1007/978-3-319-50832-0_33
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