Automated analysis of diabetic retinopathy images: Principles, recent developments, and emerging trends

Baoxin Li, Helen K. Li

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes. Timely diagnosis and intervention are essential for treatment that reduces the risk of vision loss. A good color retinal (fundus) photograph can be used as a surrogate for face-to-face evaluation of DR. The use of computers to assist or fully automate DR evaluation from retinal images has been studied for many years. Early work showed promising results for algorithms in detecting and classifying DR pathology. Newer techniques include those that adapt machine learning technology to DR image analysis. Challenges remain, however, that must be overcome before fully automatic DR detection and analysis systems become practical clinical tools.

Original languageEnglish (US)
Pages (from-to)453-459
Number of pages7
JournalCurrent diabetes reports
Volume13
Issue number4
DOIs
StatePublished - Aug 1 2013

Keywords

  • Computer-aided diagnosis
  • Diabetic retinopathy
  • Fundus photography
  • Image analysis
  • Machine learning

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism

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