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Proceedings Paper

Automated measurement of retinal blood vessel tortuosity
Author(s): Vinayak Joshi; Joseph M. Reinhardt; Michael D. Abramoff
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Paper Abstract

Abnormalities in the vascular pattern of the retina are associated with retinal diseases and are also risk factors for systemic diseases, especially cardiovascular diseases. The three-dimensional retinal vascular pattern is mostly formed congenitally, but is then modified over life, in response to aging, vessel wall dystrophies and long term changes in blood flow and pressure. A characteristic of the vascular pattern that is appreciated by clinicians is vascular tortuosity, i.e. how curved or kinked a blood vessel, either vein or artery, appears along its course. We developed a new quantitative metric for vascular tortuosity, based on the vessel's angle of curvature, length of the curved vessel over its chord length (arc to chord ratio), number of curvature sign changes, and combined these into a unidimensional metric, Tortuosity Index (TI). In comparison to other published methods this method can estimate appropriate TI for vessels with constant curvature sign and vessels with equal arc to chord ratios, as well. We applied this method to a dataset of 15 digital fundus images of 8 patients with Facioscapulohumeral muscular dystrophy (FSHD), and to the other publically available dataset of 60 fundus images of normal cases and patients with hypertensive retinopathy, of which the arterial and venous tortuosities have also been graded by masked experts (ophthalmologists). The method produced exactly the same rank-ordered list of vessel tortuosity (TI) values as obtained by averaging the tortuosity grading given by 3 ophthalmologists for FSHD dataset and a list of TI values with high ranking correlation with the ophthalmologist's grading for the other dataset. Our results show that TI has potential to detect and evaluate abnormal retinal vascular structure in early diagnosis and prognosis of retinopathies.

Paper Details

Date Published: 9 March 2010
PDF: 9 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76243A (9 March 2010); doi: 10.1117/12.844641
Show Author Affiliations
Vinayak Joshi, The Univ. of Iowa (United States)
Joseph M. Reinhardt, The Univ. of Iowa (United States)
Michael D. Abramoff, The Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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