title: Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.
abstract: 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 diagnosing glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features. The features are then used to train classifers for automatically distinguishing CSLT images of normal and glaucoma patient. As a first, in this paper, we present investigations in feature subset selction methods for reducing the relatively large input space produced by the moment methods. We use neural networks and support vector machines to determine a sub-set of moments that offer high classification accuracy. We demonstratee the efficacy of our methods to discriminate between healthy and glaucomatous optic disks based on shape information automatically derived from optic disk topography and reflectance images.
authors.affiliation: Health Informatics Lab, Faculty of Computer Science, Dalhousie University, Halifax, Canada.
authors.forename: Jin
authors.initials: J
authors.lastname: Yu
authors.forename: Syed Sibte Raza
authors.initials: SS
authors.lastname: Abidi
authors.forename: Paul
authors.initials: P
authors.lastname: Artes
authors.forename: Andy
authors.initials: A
authors.lastname: McIntyre
authors.forename: Malcolm
authors.initials: M
authors.lastname: Heywood
mesh_terms: D057225:Data Mining; D003941:Diagnostic Techniques, Ophthalmological; D005901:Glaucoma; D006801:Humans; D007834:Lasers; D016571:Neural Networks, Computer; D009898:Optic Disk; D014054:Tomography