Automated optic nerve analysis for diagnostic support in glaucoma

Jin Yu, Syed Sibte Raza Abidi, Paul Habib Artes, Andrew McIntyre, Malcolm Heywood

    Research output: Contribution to journalArticlepeer-review


    The availability of modern imaging techniques such as Confocal Scanning Laser Tomography (CSLT) for capturing high-quality optic nerve images offer the potential for developing automatic and objective methods for supporting clinical decision-making in glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features, and the use of these features to train classifiers for automatically distinguishing CSLT images of healthy and diseased optic nerves. As a first step, in this paper, we present investigations in feature subset selection methods for reducing the relatively large input space produced by the moment methods. Our results demonstrate that our methods discriminate between healthy and glaucomatous optic nerves based on shape information automatically derived from CSLT tomography images. © 2005 IEEE.
    Original languageEnglish
    Pages (from-to)97-102
    Number of pages5
    JournalIEEE Symposium on Computer - Based Medical Systems. Proceedings
    Publication statusPublished - 2005


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