IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local adaptive algorithm for microaneurysms detection in digital fundus images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
IEEE Transactions on Information Technology in Biomedicine
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Accurate retinal blood vessel detection offers a great opportunity to predict and detect the stages of various ocular and systemic diseases, such as glaucoma, hypertension and congestive heart failure, since the change in width of blood vessels in retina has been reported as an independent and significant prospective risk factor for such diseases. In large-population studies of disease control and prevention, there exists an overwhelming need for an automatic tool that can reliably and accurately identify and measure retinal vessel diameters. To address requirements in this clinical setting, a vessel detection algorithm is proposed to quantitatively measure the salient properties of retinal vessel and combine the measurements by Bayesian decision to generate a confidence value for each detected vessel segment. The salient properties of vessels provide an alternative approach for retinal vessel detection at a level higher than detection at the pixel level. Experiments show superior detection performance than currently published results using a publicly available data set. More importantly, the proposed algorithm provides the confidence measurement that can be used as an objective criterion to select reliable vessel segments for diameter measurement.