December 2013
Volume 13, Issue 15
Free
OSA Fall Vision Meeting Abstract  |   October 2013
Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images
Author Affiliations
  • Piero Rangel-Fonseca
    Centro de Investigaciones en Óptica, A.C., Leon, Gto., Mexico
  • Armando Gómez-Vieyra
    Laboratorio de SistemasComplejos, Departamento de CienciasBásicas, UniversidadAutónomaMetropolitana, UnidadAzcapotzalco, Azcapotzalco, D.F., Mexico
  • Daniel Malacara-Hernández
    Centro de Investigaciones en Óptica, A.C., Leon, Gto., Mexico
  • Mario Wilson
    Laboratoire de Physique des Lasers, AtomesetMolécules, UMR-CNRS 8523, Université Lille, Villeneuve d'AscqCedex, France
  • David Williams
    Center for Visual Science, University of Rochester, Rochester, New York, USA
    The Institute of Optics, University of Rochester, Rochester, New York, USA
  • Ethan Rossi
    Center for Visual Science, University of Rochester, Rochester, New York, USA
Journal of Vision October 2013, Vol.13, P33. doi:10.1167/13.15.68
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      Piero Rangel-Fonseca, Armando Gómez-Vieyra, Daniel Malacara-Hernández, Mario Wilson, David Williams, Ethan Rossi; Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images. Journal of Vision 2013;13(15):P33. doi: 10.1167/13.15.68.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Adaptive optics (AO) imaging methods allow for the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics in the living eye.

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