Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2013, Article ID 260410, 16 pages http://dx.doi.org/10.1155/2013/260410 Research Article Automatic Segmentation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation Yi Yin, Mouloud Adel, and Salah Bourennane Institut Fresnel, Ecole Centrale de Marseille, Aix-Marseille Universit´ e, Domaine Universitaire de Saint-J´ erˆ ome, 13397 Marseille, France Correspondence should be addressed to Yi Yin; yi.yin@inria.fr Received 2 August 2013; Accepted 21 October 2013 Academic Editor: Seiya Imoto Copyright © 2013 Yi Yin et al. Tis 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. Te 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 diferent 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 identifcation. Te sensitivity and specifcity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specifcity is up to 0.9710. 1. Introduction Automatic vessel segmentation in medical images is a very important task in many clinical investigations. In ophthal- mology, the early diagnosis of several pathologies such as arterial hypertension, arteriosclerosis, diabetic retinopathy, cardiovascular disease, and stroke [1, 2] could be achieved by analyzing changes in blood vessel patterns such as tortuosity, bifurcation, and variation of vessel width on retinal images. Early detection and characterization of retinal blood vessels are needed for a better and efective treatment of diseases. Hence, computer-aided detection and analysis of retinal images could help doctors, allowing them to use a quantitative tool for a better diagnosis, especially when analyzing a huge amount of retinal images in screening programs. Many methods for blood vessel detection on retinal images have been reported in the literature [3–5]. Tese techniques can be roughly classifed into pixel-based methods [6–14], model-based methods [15–21], and tracking-based approaches [22–29], respectively. Pixel-based approaches consist in convolving the image with a spatial flter and then assigning each pixel to back- ground or vessel region, according to the result of a second processing step such as thresholding or morphological oper- ation. Chaudhuri et al. [8] used 2D Gaussian kernels with 12 orientations, retaining the maximum response. Hoover et al. [6] improved this technique by computing local features to assign regions to vessel or background. A multithreshold scheme was used by Jiang and Mojon [9], whereas Sofa and Stewart [10] presented a multiscale matched flter. Zana and Klein [11] used morphological flter combined with curvature evaluation for retinal segmentation. Neimeijer et al. [12] used a classifcation scheme based on a simple feature computa- tion. Gabor wavelet transform with a Bayesian classifcation is performed by Soares et al. [13]. Staal et al. [7] applied a supervised classifcation based on features computed near the centerline. Te same scheme was used by Ricci and Perfetti [14] but with a modifed line operator to train a supervised pixel classifer. Model-based approaches use parametric models to extract vessels. Al-Diri et al. [30] extracted blood vessel segment profles and measured vessel width using a para- metric active contour, based on two contours coupled by spring models. Active contour model was applied by Kozerke et al. [15] to automatically segment vessels. A level set geometric-based regularization approach is given by Gooya et al. [16]. Vasilevskiy and Siddiqi [17] developed FLUX for