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In this paper we propose a novel automated glaucoma detection framework for mass-screening that operates on inexpensive retinal cameras. The proposed methodology is based on the assumption that discriminative features for glaucoma diagnosis can be extracted from the optical nerve head structures,
such as the cup-to-disc ratio or the neuro-retinal rim variation. After automatically segmenting the cup and optical disc, these features are feed into a machine learning classifier. Experiments were performed using two different datasets and from the obtained results the proposed technique provides
better performance than approaches based on appearance. A main advantage of our approach is that it only requires a few training samples to provide high accuracy over several different glaucoma stages.
Original languageEnglish
Number of pages6
Publication statusPublished - 27 Aug 2014
EventIrish Machine Vision and Image Processing Conference - Intelligent Systems Research Centre, Derry, United Kingdom
Duration: 27 Aug 201429 Aug 2014


ConferenceIrish Machine Vision and Image Processing Conference
Abbreviated titleIMVIP 2014
Country/TerritoryUnited Kingdom
Internet address


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