Contextual detection of diabetic pathology in wide-field retinal angiograms.

Colin R. Buchanan, Emanuele Trucco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationConference proceedings of the IEEE Engineering in Medicine and Biology Society
Pages5437-5440
Number of pages4
Volume2008
Publication statusPublished - 2008

Fingerprint Dive into the research topics of 'Contextual detection of diabetic pathology in wide-field retinal angiograms.'. Together they form a unique fingerprint.

Cite this