Contextual detection of diabetic pathology in wide-field retinal angiograms

Colin R. Buchanan, Emanuele Trucco

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

    8 Citations (Scopus)

    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 ultra-wide-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 publication2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8
    Place of PublicationNEW YORK
    PublisherIEEE Computer Society
    Pages5437-5440
    Number of pages4
    ISBN (Print)978-1-4244-1814-5
    Publication statusPublished - 2008
    Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Vancouver, Canada
    Duration: 20 Aug 200825 Aug 2008

    Conference

    Conference30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    CountryCanada
    CityVancouver
    Period20/08/0825/08/08

    Keywords

    • FLUORESCEIN ANGIOGRAMS
    • OCULAR FUNDUS
    • LEAKAGE
    • QUANTIFICATION
    • IMAGES

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