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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 260410, 16 pages
Research Article

Automatic Segmentation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation

Institut Fresnel, Ecole Centrale de Marseille, Aix-Marseille Université, Domaine Universitaire de Saint-Jérôme, 13397 Marseille, France

Received 2 August 2013; Accepted 21 October 2013

Academic Editor: Seiya Imoto

Copyright © 2013 Yi Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.