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.