Automatic Photoreceptor Detection in In-Vivo Adaptive Optics Retinal Images: Statistical Validation

Kevin Loquin (1) , Isabelle Bloch (2) , Kiyoko Nakashima (3) , Florence Rossant (4) , Pierre-Yves Boëlle (5) , Michel Paques (3)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 CHNO - Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts
4 ISEP - Institut Supérieur d'Electronique de Paris
5 Epidémiologie, Systèmes d'Information, Modélisation

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.

Mots clés: in vivo diagnosis; Photoreceptor detection; Retina imaging; Adaptive Optics

Domaines: Traitement du signal et de l'image [eess.SP]

Kevin Loquin, Isabelle Bloch, Kiyoko Nakashima, Florence Rossant, Pierre-Yves Boëlle, et al.. Automatic Photoreceptor Detection in In-Vivo Adaptive Optics Retinal Images: Statistical Validation. ICIAR, Jun 2012, Aveiro, Portugal. ⟨10.1007/978-3-642-31298-4_48⟩. ⟨hal-02882938⟩