Contextual detection of diabetic pathology in wide-field retinal angiograms.
We report a novel algorithm to locate vascular leakage and ischemia in retinal angiographic image sequences leveraging contextual knowledge of co-occurring pathologies. The key contributions are the use of spatio-temporal features exploiting the evolution of intensity levels over the sequence and contextual knowledge to detect ischemia. The specific nature of these diseased regions is determined using an AdaBoost learning algorithm. Training was performed with a varied set of 16 ground-truth image sequences, and testing on unseen images. The images used were acquired with an Optos ultrawide-field scanning laser ophthalmoscope. Evaluation against manual annotations demonstrates successful location of 93% of leakage regions and 70% of ischemic regions.
Buchanan
Colin R
CR
School of Engineering and Physical Sciences, Heriot-Watt University, EH144AS, UK. c.r.buchanan@hw.ac.uk
Trucco
Emanuele
E
D000465:Algorithms; D001185:Artificial Intelligence; D003930:Diabetic Retinopathy / Q000473:pathology*; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity