Abstract
This article presents a photoreceptor detection algorithm applied to in-vivo Adaptive Optics (AO) images of the retina obtained from an advanced ophthalmic diagnosis device. Our algorithm is based on a recursive construction of thresholded connected components when the seeds of the recursions are the regional maxima of the deconvoluted image. This algorithm is validated on a gold standard dataset obtained thanks to manual cones detections made by ophtalmologist physicians. © 2012 Springer-Verlag.
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CITATION STYLE
Loquin, K., Bloch, I., Nakashima, K., Rossant, F., Boelle, P. Y., & Paques, M. (2012). Automatic photoreceptor detection in in-vivo adaptive optics retinal images: Statistical validation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7325 LNCS, pp. 408–415). https://doi.org/10.1007/978-3-642-31298-4_48
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