Spatio-temporal characterization of vessel segments applied to retinal angiographic images

Bouaoune, Y.; Assogba, Marc Kokou; Nunes, Jean‐Claude; Bunel, P.

doi:10.1016/s0167-8655(02)00280-5

Cited by 2 publications

(1 citation statement)

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“…The second one is based on tracking from these seed points and combining the results of local vessel segmentation to get the final detected vascular tree. Local vessel segmentation [34][35][36] is the foundation to segment the whole vascular tree. Our study focuses on this second stage, which takes into account local vessel detection, including the vessel edges and centerline detection.…”

Section: Introductionmentioning

confidence: 99%

Retinal vessel segmentation using a probabilistic tracking method

Yin

Adel²,

Bourennane³

2012

Pattern Recognition

141

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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.

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Section: Introductionmentioning

confidence: 99%