title: Measurement and classification 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. Finally, we discuss how the accuracy of these measures can be influence by the method used to extract the blood vessel segments.
authors: Department of Applied and Numerical Mathematics, Sandia National Laboratories, Alburquerque, NM 87185, USA. wehart@cs.sandia.gov
authors: W E
authors: WE
authors: Hart
authors: M
authors: M
authors: Goldbaum
authors: B
authors: B
authors: Côté
authors: P
authors: P
authors: Kube
authors: M R
authors: MR
authors: Nelson
mesh_terms: D003936:Diagnosis, Computer-Assisted; D005654:Fundus Oculi; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D012372:ROC Curve; D012164:Retinal Diseases / Q000175:diagnosis*; D012171:Retinal Vessels