%0 Journal Article
%T Automatic Segmentation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation
%A Yi Yin
%A Mouloud Adel
%A Salah Bourennane
%J Computational and Mathematical Methods in Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/260410
%X 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. 1. Introduction Automatic vessel segmentation in medical images is a very important task in many clinical investigations. In ophthalmology, 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 effective 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¨C5]. These techniques can be roughly classified into pixel-based methods [6¨C14], model-based methods [15¨C21], and tracking-based approaches [22¨C29], respectively. Pixel-based approaches consist in convolving the image with a spatial filter and then assigning each pixel to background or vessel region, according to the result of a second processing step such as thresholding or morphological operation. 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 Sofka and Stewart [10] presented a multiscale matched filter. Zana and Klein
%U http://www.hindawi.com/journals/cmmm/2013/260410/