title: Automated quality evaluation of digital fundus photographs.
abstract: OBJECTIVE
  Retinal images acquired by means of digital photography are often used for evaluation and documentation of the ocular fundus, especially in patients with diabetes, glaucoma or age-related macular degeneration. The clinical usefulness of an image is highly dependent on its quality. We set out to develop and evaluate an automatic method of evaluating the quality of digital fundus photographs.
  
  
  METHODS
  A method for making a numerical quantification of image sharpness and illumination was developed using Matlab image analysis functions. Based on their sharpness and illumination measures, 1000 fundus photographs, randomly selected from a clinical database, were assigned to four predefined quality groups (not acceptable, acceptable, good, very good). Six independent observers, comprising three experienced ophthalmologists and three ophthalmic nurses with extensive experience in fundus image acquisition, classified a selection of 100 of these images into the corresponding quality groups.
  
  
  RESULTS
  Automatic quality evaluation was more sensitive than evaluation by human observers in terms of ability to discriminate between good and very good images. The median concordance between the six human observers and the automatic evaluation was substantial (kappa = 0.64).
  
  
  CONCLUSIONS
  The proposed method provides an objective quality assessment of digital fundus photographs which agrees well with evaluations made by qualified human observers and which may be useful in clinical practice.
authors.affiliation: Department of Clinical Neuroscience, Ophthalmology and Vision, Karolinska Institute, Stockholm, Sweden.
authors.forename: Herman
authors.initials: H
authors.lastname: Bartling
authors.forename: Peter
authors.initials: P
authors.lastname: Wanger
authors.forename: Lene
authors.initials: L
authors.lastname: Martin
mesh_terms: D001331:Automation; D003952:Diagnostic Imaging / Q000379:methods*; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted; D010781:Photography / Q000592:standards*; D012160:Retina / Q000473:pathology; D012680:Sensitivity and Specificity