Abstract
Optic nerve head detection is a fundamental step in automated retinal image analysis algorithms. In this paper, we propose a new optic nerve head detection algorithm that is based on the efficient analysis of multi-orientation patterns. To this end, we make use of invertible orientation scores, which are functions on the Euclidean motion group. We apply the classical and fast approach of template matching via cross-correlation, however, we do this in the domain of an orientation score rather than the usual image domain. As such, this approach makes it possible to efficiently detect multi-orientation patterns. The method is extensively tested on public and private databases and we show that the method is generically applicable to images originating from traditional fundus cameras as well as from scanning laser ophthalmoscopes.
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Bekkers, E., Duits, R., ter Haar Romeny, B. (2014). Optic Nerve Head Detection via Group Correlations in Multi-orientation Transforms. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_33
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DOI: https://doi.org/10.1007/978-3-319-11755-3_33
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