title: Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.
abstract: The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.
authors.affiliation: Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Vinayak S
authors.initials: VS
authors.lastname: Joshi
authors.affiliation: Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Reinhardt
authors.affiliation: Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America ; The Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, Iowa, United States of America.
authors.forename: Mona K
authors.initials: MK
authors.lastname: Garvin
authors.affiliation: Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States of America ; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States of America ; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States of America ; The Iowa City Veteran's Medical Center, Iowa City, Iowa, United States of America.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abramoff
mesh_terms: D000465:Algorithms; D001331:Automation; D016000:Cluster Analysis; D003116:Color; D016208:Databases, Factual; D005654:Fundus Oculi; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012160:Retina / Q000502:physiology*; D012161:Retinal Artery / Q000502:physiology*; D012169:Retinal Vein / Q000502:physiology*