title: Automated measurement of retinal vascular tortuosity.
abstract: Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity.
authors.affiliation: Applied & Numerical Math Dept., Sandia National Laboratories. wehart@cs.sandia.gov
authors.forename: W E
authors.initials: WE
authors.lastname: Hart
authors.forename: M
authors.initials: M
authors.lastname: Goldbaum
authors.forename: B
authors.initials: B
authors.lastname: Côté
authors.forename: P
authors.initials: P
authors.lastname: Kube
authors.forename: M R
authors.initials: MR
authors.lastname: Nelson
mesh_terms: D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016015:Logistic Models; D012164:Retinal Diseases / Q000145:classification*; D012171:Retinal Vessels / Q000473:pathology*