NDLI: An experimental assessment of five indices of retinal vessel tortuosity with the RET-TORT public dataset

Content Provider	IEEE Xplore Digital Library
Author	Lisowska, A. Annunziata, R. Loh, G.K. Karl, D. Trucco, E.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput., Univ. of Dundee, Dundee, UK (Lisowska, A.; Annunziata, R.; Trucco, E.) \|\| Dept. of Ophthalmology, Univ. Hosp., Dundee, UK (Loh, G.K.; Karl, D.)
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 slope-chain 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, RETTORT. We find that (1) the tortuosity density index offers good performance overall, but is not always the best performer; (2) curvature-based methods are the best if high-frequency resampling is possible, but (3) are the most sensitive to variations of the number of samples; (4) slope-chain coding performs best at low sampling rates, but the length of the linear elements must be chosen carefully. In general, performance may vary considerably with resampling, suggesting that the choice of a tortuosity index for clinical inference requires attention to numerical details, and ideally standardization thereof.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	5414
Ending Page	5417
File Size	1953358
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexes Retinal vessels Arteries Correlation Veins Diseases
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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