Identifying All True Vessels from Segmented Retinal Images
Keywords:
Ophthalmology, optimal vessel forest, retinal image analysis, simultaneous vessel identification, vascular structureAbstract
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 Both the arteries and veins of the retina are generally binary trees, whose properties can be considered either locally or globally. Measurable geometrical changes in diameter, branching angle, length, or tortuosity, as a result of disease, have been described in retinal blood vessels. The detection and measurement of retinal blood vessels can be used to quantify the severity of disease such as hypertension, stroke and arteriosclerosis, as part of the process of automated diagnosis of disease or in the assessment of the progression of therapy. Thus, a reliable method of vessel detection and quantification would be valuable. In this paper, we address the problem of identifying true vessels as a post processing 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.
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