2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2014
DOI: 10.1109/embc.2014.6944850
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An experimental assessment of five indices of retinal vessel tortuosity with the RET-TORT public dataset

Abstract: Abstract-We compare the performance of five indices of retinal vessel tortuosity against sampling rates of vessel centerlines. We consider distance measure, tortuosity density, two curvature-based measures, and a recently introduced slopechain coding for general curves, never before assessed comparatively with retinal vessels. To enable replication of our results, we use the public dataset for retinal tortuosity, RET-TORT. We find that (1) the tortuosity density index offers good performance overall, but is no… Show more

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Cited by 35 publications
(28 citation statements)
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“…Recent results indicate that the number of inflection points (at which the sign of the curvature changes) and curvature along the fibre or vessel are relevant tortuosity definitions [13], hence we include them in our dictionary of features. Accurate curvature estimation from samples is a well-known tricky task [14][15][16].…”
Section: Multiple-scale Fibre-level Features Extractionmentioning
confidence: 99%
“…Recent results indicate that the number of inflection points (at which the sign of the curvature changes) and curvature along the fibre or vessel are relevant tortuosity definitions [13], hence we include them in our dictionary of features. Accurate curvature estimation from samples is a well-known tricky task [14][15][16].…”
Section: Multiple-scale Fibre-level Features Extractionmentioning
confidence: 99%
“…Several definitions have been proposed with the aim of making tortuosity estimation objective (see Lisowska et al (2014) for a comparative study on retinal blood vessels).…”
Section: Tortuosity Estimationmentioning
confidence: 99%
“…Fraz et al [17] proposed thresholding method to classify pixel into vessel and non-vessel. This method can classify image into separate component which changes the image into a binary image.…”
Section: Thresholdingmentioning
confidence: 99%