Contextual detection of ischemic regions in ultra-wide-field-of-view retinal fluorescein angiograms.
We report a novel prototype algorithm using contextual knowledge to locate ischemic regions in ultra-wide-field-of-view retinal fluorescein angiograms. We use high-resolution images acquired by an Optos ultra-wide-field-of-view (more than 200 degrees) scanning laser ophthalmoscope. We leverage the simultaneous occurrence of ischemia with a number of other signs, detected automatically, typical for the state of progress of the condition in a diabetic patient. The specific nature of ischemic and non-ischemic regions is determined with an AdaBoost learning algorithm. Preliminary results demonstrate above 80% pixel classification accuracy against manual annotations.
Trucco
E
E
School of Computing, University of Dundee, UK. e.trucco@hw.ac.uk
Buchanan
C R
CR
Aslam
T
T
Dhillon
B
B
D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007511:Ischemia / Q000175:diagnosis*; D012171:Retinal Vessels / Q000473:pathology