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|Title:||Simultaneously identifying all true vessels from segmented retinal images||Authors:||Lau, Q.P.
optimal vessel forest
retinal image analysis
simultaneous vessel identification
|Issue Date:||2013||Citation:||Lau, Q.P., Lee, M.L., Hsu, W., Wong, T.Y. (2013). Simultaneously identifying all true vessels from segmented retinal images. IEEE Transactions on Biomedical Engineering 60 (7) : 1851-1858. ScholarBank@NUS Repository. https://doi.org/10.1109/TBME.2013.2243447||Abstract:||Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a postprocessing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy. © 1964-2012 IEEE.||Source Title:||IEEE Transactions on Biomedical Engineering||URI:||http://scholarbank.nus.edu.sg/handle/10635/77919||ISSN:||00189294||DOI:||10.1109/TBME.2013.2243447|
|Appears in Collections:||Staff Publications|
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