Automated detection of exudates for diabetic retinopathy screening.
Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13,219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.
Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD, UK. a.fleming@abdn.ac.uk
Alan D
AD
Fleming
Sam
S
Philip
Keith A
KA
Goatman
Graeme J
GJ
Williams
John A
JA
Olson
Peter F
PF
Sharp
D001185:Artificial Intelligence; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003941:Diagnostic Techniques, Ophthalmological; D005122:Exudates and Transudates; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008403:Mass Screening / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012160:Retina / Q000473:pathology; D015593:Retinal Drusen; D012606:Scotland; D012680:Sensitivity and Specificity