Bayesian detection of the fovea in eye fundus angiographies
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatio-temporal characterization of vessel segments applied to retinal angiographic images
Pattern Recognition Letters
Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
A Trained Spin-Glass Model for Grouping of Image Primitives
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved algorithm for vessel centerline tracking in coronary angiograms
Computer Methods and Programs in Biomedicine
Vessel enhancement filter using directional filter bank
Computer Vision and Image Understanding
Maximum likelihood estimation of vessel parameters from scale space analysis
Image and Vision Computing
IEEE Transactions on Information Technology in Biomedicine
Robust model-based vasculature detection in noisy biomedical images
IEEE Transactions on Information Technology in Biomedicine
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Vessel structures such as retinal vasculature are important features for computer-aided 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.