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KA Vermeer, FM Vos, AM Vossepoel, HG Lemij; Improved Blood Vessel Detection for Retinal GDx Images With a Novel Model Based Method . Invest. Ophthalmol. Vis. Sci. 2002;43(13):999.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose: The GDx (Laser Diagnostic Technologies, San Diego, CA) is a scanning laser polarimeter designed to measure retinal nerve fiber layer thickness in vivo. It has a built-in blood vessel detection algorithm that is used to disregard spurious readings on blood vessel locations, which would otherwise adversely affect the parameter calculations. Since the standard algorithm misses some vessels, comprising those with specular reflection, we designed a better, model-based method. Methods: Blood vessels show up as either single dark line-like structures or two parallel dark line-like structures separated by a specular reflex. We used a simple brightness gap to model the vessel or vessel walls, detect them with a Laplace and thresholding technique and later fill in the inner part. This approach allows for optimization of various parameters according to the specific image properties. We tested our algorithm by making a pixel-by-pixel comparison between the outcome of the algorithm and ten hand-labeled images. Results: Our algorithm yielded a sensitivity of 93% at a specificity of 91%. The sensitivity and specificity of the built-in GDx algorithm were 66% and 97.6%, respectively. Conclusions: Our algorithm outperformed the GDx on sensitivity, without compromising much on specificity. To reduce spurious readings over blood vessel, we think that the sensitivity should be higher than a good specificity. We therefore think that our method can help to improve GDx performance.
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