title: Human Vision-Motivated Algorithm Allows Consistent Retinal Vessel Classification Based on Local Color Contrast for Advancing General Diagnostic Exams.
abstract: OBJECTIVE
  Abnormalities of blood vessel anatomy, morphology, and ratio can serve as important diagnostic markers for retinal diseases such as AMD or diabetic retinopathy. Large cohort studies demand automated and quantitative image analysis of vascular abnormalities. Therefore, we developed an analytical software tool to enable automated standardized classification of blood vessels supporting clinical reading.
  
  
  METHODS
  A dataset of 61 images was collected from a total of 33 women and 8 men with a median age of 38 years. The pupils were not dilated, and images were taken after dark adaption. In contrast to current methods in which classification is based on vessel profile intensity averages, and similar to human vision, local color contrast was chosen as a discriminator to allow artery vein discrimination and arterial-venous ratio (AVR) calculation without vessel tracking.
  
  
  RESULTS
  With 83% ± 1 standard error of the mean for our dataset, we achieved best classification for weighted lightness information from a combination of the red, green, and blue channels. Tested on an independent dataset, our method reached 89% correct classification, which, when benchmarked against conventional ophthalmologic classification, shows significantly improved classification scores.
  
  
  CONCLUSIONS
  Our study demonstrates that vessel classification based on local color contrast can cope with inter- or intraimage lightness variability and allows consistent AVR calculation. We offer an open-source implementation of this method upon request, which can be integrated into existing tool sets and applied to general diagnostic exams.
authors.affiliation: Vision Rehabilitation Research Unit Centre for Ophthalmology, University Eye-Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany 2Division of Experimental Ophthalmology, University of Tübingen, Centre for Ophthalmology, Institute for Ophtha.
authors.forename: Iliya V
authors.initials: IV
authors.lastname: Ivanov
authors.affiliation: University Eye-Hospital, Centre for Ophthalmology, Eberhard Karls University of Tübingen, Tübingen, Germany.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Leitritz
authors.affiliation: Klinikum am Steinenberg, Department of Obstetrics and Gynecology, District Hospital Reutlingen, Reutlingen, Germany.
authors.forename: Lars A
authors.initials: LA
authors.lastname: Norrenberg
authors.affiliation: University Eye-Hospital, Centre for Ophthalmology, Eberhard Karls University of Tübingen, Tübingen, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Völker
authors.affiliation: Zentrale Systeme, Zentrum für Datenverarbeitung, University of Tübingen, Tübingen, Germany.
authors.forename: Marek
authors.initials: M
authors.lastname: Dynowski
authors.affiliation: Division of Experimental Ophthalmology, University of Tübingen, Centre for Ophthalmology, Institute for Ophthalmic Research, Tübingen, Germany.
authors.forename: Marius
authors.initials: M
authors.lastname: Ueffing
authors.affiliation: Division of Experimental Ophthalmology, University of Tübingen, Centre for Ophthalmology, Institute for Ophthalmic Research, Tübingen, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Dietter
mesh_terms: D000328:Adult; D000465:Algorithms; D002965:Classification / Q000379:methods*; D003116:Color; D003623:Dark Adaptation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012161:Retinal Artery / Q000033:anatomy & histology* / Q000166:cytology / Q000473:pathology; D012164:Retinal Diseases / Q000175:diagnosis* / Q000473:pathology; D012169:Retinal Vein / Q000033:anatomy & histology* / Q000166:cytology / Q000473:pathology; D012984:Software