The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Novel Method for the Automatic Grading of Retinal Vessel Tortuosity
Abstract
Tortuosity is among the first alterations in the retinal vessel network to appear in many retinopathies, such as those due to hypertension. An automatic evaluation of retinal vessel tortuosity would help the early detection of such retinopathies. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. This justifies the need for a new definition, able to express in mathematical terms the tortuosity as perceived by ophthalmologists. We propose here a new algorithm for the evaluation of tortuosity in vessels recognized in digital fundus images. It is based on partitioning each vessel in segments of constant-sign curvature and then combining together each evaluation of such segments and their number. The algorithm has been compared with other available tortuosity measures on a set of 30 arteries and one of 30 veins from 60 different images. These vessels had been preliminarily ordered by a retina specialist by increasing perceived tortuosity. The proposed algorithm proved to be the best one in matching the clinically perceived vessel tortuosity.
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Citations
Cites
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Cited by
Brown, J. M., Campbell, J. P., Beers, A., Chang, K., Ostmo, S., Chan, R. V. P., Dy, J., Erdogmus, D., Ioannidis, S., Kalpathy-Cramer, J., et al. (2018) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2018.1934 · Cited by 657
Twisted Blood Vessels: Symptoms, Etiology and Biomechanical Mechanisms
Han, H.-C. (2012) · Journal of Vascular Research · doi:10.1159/000335123 · Cited by 489
Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., & Angelopoulou, E. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0455 · Cited by 472
Owen, C. G., Rudnicka, A. R., Mullen, R., Barman, S. A., Monekosso, D., Whincup, P. H., Ng, J., & Paterson, C. (2009) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.08-3018 · Cited by 465
Metadata
- Authors
- Grisan, E., Foracchia, M., & Ruggeri, A.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2008-03
- Volume
- 27
- Issue
- 3
- Pages
- 310-319
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2140431812
- Cited by
- 260
- References
- 1
- FWCI
- 6.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Grisan, E., Foracchia, M., & Ruggeri, A. (2008). A Novel Method for the Automatic Grading of Retinal Vessel Tortuosity. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2007.904657
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
