Retinal vessel segmentation using a probabilistic tracking method

Abstract

Vessel structures such as retinal vasculature are important features for computeraided diagnosis. In this paper, a probabilistic tracking method is proposed to detect blood vessels in retinal images. During the tracking process, vessel edge points are detected iteratively using local grey level statistics and vessel's continuity properties. At a given step, a statistic sampling scheme is adopted to select a number of vessel edge points candidates in a local studying area. Local vessel's sectional intensity profiles are estimated by a Gaussian shaped curve. A Bayesian method with the Maximum a posteriori (MAP) probability criterion is then used to identify local vessel's structure and find out the edge points from these candidates. Evaluation is performed on both simulated vascular and real retinal images. Different geometric shapes and noise levels are used for computer simulated images, whereas real retinal images from the REVIEW database are tested. Evaluation performance is done using the Segmentation Matching Factor (SMF) as a quality parameter. Our approach performed better when comparing it with Sun's and Chaudhuri's methods. ROC curves are also plotted, showing effective detection of retinal blood vessels (true positive rate) with less false detection (false positive rate) than Sun's method.

Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

“…The performance of the thresholded median-offset normalized Gabor filter is comparable with previous methods. The sensitivity of the medianoffset approach outperforms some of the recent studies such as that of Yin et al [12] on the DRIVE data set. Performance on the STARE data set is not as competitive against recent studies.…”

“…Siddalingaswamy et al [33] recommend a wavelength of 4 pixels as ideal for detecting most vessels within fundus images. In this study, wavelengths of 4, 8 and 12 pixels are presented as wavelengths within the range [4,12] showed the highest effectiveness in our experiments for both thick and thin vessel detection. Sensitivity reflects the rate of accurate vessel detection and the median-offset method shows the highest sensitivity in the context of a high specificity.…”

“…Supervised segmentation is dependent on a training set and comprises methods such as Support Vector Machines (SVM) [8] and neural networks [9], [10]. Unsupervised segmentation is independent of a training set and is exemplified by rule-based methods such as vessel tracking [11], [12] and thresholding [6], [13]. The focus of thresholding schemes is the selection of a gray level that optimally segments an object from its background.…”

Automatic Generation of Synthetic Retinal Fundus Images: Vascular Network

“…Retinal Image Analysis (RIA) aims to develop computational and mathematical techniques for helping clinicians with the diagnosis of diseases such as diabetes, glaucoma and cardiovascular conditions, that may cause changes in retinal blood vessel patterns like tortuosity, bifurcations, variation of vessel width and colour 1,2 . RIA algorithms have to be validated to avoid obtaining misleading results.…”

An Automated Graphical User Interface based System for the Extraction of Retinal Blood Vessels using Kirsch’s Template

“…According to Muthukrishnan & Radha [1], the classical methods of edge detection operates on convolving the image through an operator. Yin et al [2] proposed a probabilistic tracking method to detect blood vessels in retinal images. They categorized the task of vessel extraction into two main groups: Pixel-based methods and tracking methods.…”