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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 401413, 9 pages
Research Article

Bayesian Method with Spatial Constraint for Retinal Vessel Segmentation

Institut Fresnel/UMR-CNRS, D. U. de Saint-Jérôme, 13013 Marseille, France

Received 20 March 2013; Revised 22 May 2013; Accepted 10 June 2013

Academic Editor: William Crum

Copyright © 2013 Zhiyong Xiao 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.


A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.