Automated assessment of diabetic retinal image quality based on clarity and field definition

Alan D Fleming, Sam Philip, Keith A Goatman, John A Olson, Peter F Sharp

Research output: Contribution to journalArticlepeer-review

157 Citations (Scopus)


PURPOSE. To evaluate the performance of an automated retinal image quality assessment system for use in automated diabetic retinopathy grading.

METHODS. Algorithmic methods have been developed for assessing the quality of 45 degrees single field retinal images for use in diabetic retinopathy screening. For this purpose, image quality was defined by two aspects: image clarity and field definition. An image with adequate clarity was defined as one that shows sufficient detail for automated retinopathy grading. The visibility of the macular vessels was used as an indicator of image clarity, since these vessels are known to be narrow and become less visible with any image degradation. An image with adequate field definition was defined as one that shows the desired field of view for retinopathy grading, including the fun 45 degrees field of view, the optic disc, and at least two optic disc diameters of visible retina around the fovea. From 489 patients attending a diabetic retinopathy screening program, 1039 retinal images were obtained. The images were graded by a clinician for image clarity and field definition, with a comprehensive image-quality grading scheme.

RESULTs. The sensitivity and specificity were, respectively, 100% and 90.9% for inadequate clarity detection, 95.3% and 96.4% for inadequate field definition detection, and 99.1% and 89.4% for inadequate overall quality detection.

CONCLUSIONS. The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.

Original languageEnglish
Pages (from-to)1120-1125
Number of pages6
JournalInvestigative Ophthalmology & Visual Science
Issue number3
Publication statusPublished - Mar 2006


  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Child
  • Diabetic Retinopathy
  • Humans
  • Image Processing, Computer-Assisted
  • Middle Aged
  • Photography
  • Reproducibility of Results
  • Retina
  • Sensitivity and Specificity


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