title: Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends.
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.
authors.affiliation: School of Computing, Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA. Baoxin.Li@asu.edu
authors.forename: Baoxin
authors.initials: B
authors.lastname: Li
authors.forename: Helen K
authors.initials: HK
authors.lastname: Li
mesh_terms: D001331:Automation; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000473:pathology; D003952:Diagnostic Imaging / Q000379:methods* / Q000639:trends*; D006801:Humans; D007091:Image Processing, Computer-Assisted