title: Automated quality assessment of retinal fundus photos.
abstract: OBJECTIVE
  Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation.
  
  
  METHODS
  The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification.
  
  
  RESULTS
  On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches.
  
  
  CONCLUSIONS
  The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.
authors.affiliation: Friedrich-Alexander-University Erlangen-Nuremberg, Germany. jan.paulus@informatik.uni-erlangen.de
authors.forename: Jan
authors.initials: J
authors.lastname: Paulus
authors.forename: Jörg
authors.initials: J
authors.lastname: Meier
authors.forename: Rüdiger
authors.initials: R
authors.lastname: Bock
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.forename: Georg
authors.initials: G
authors.lastname: Michelson
mesh_terms: D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012372:ROC Curve; D012160:Retina / Q000473:pathology*; D012164:Retinal Diseases / Q000175:diagnosis*