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Biomed Opt Express ; 4(11): 2527-39, 2013.
Artículo en Inglés | MEDLINE | ID: mdl-24298413


Morgan and colleagues demonstrated that the RPE cell mosaic can be resolved in the living human eye non-invasively by imaging the short-wavelength autofluorescence using an adaptive optics (AO) ophthalmoscope. This method, based on the assumption that all subjects have the same longitudinal chromatic aberration (LCA) correction, has proved difficult to use in diseased eyes, and in particular those affected by age-related macular degeneration (AMD). In this work, we improve Morgan's method by accounting for chromatic aberration variations by optimizing the confocal aperture axial and transverse placement through an automated iterative maximization of image intensity. The increase in image intensity after algorithmic aperture placement varied depending upon patient and aperture position prior to optimization but increases as large as a factor of 10 were observed. When using a confocal aperture of 3.4 Airy disks in diameter, images were obtained using retinal radiant exposures of less than 2.44 J/cm(2), which is ~22 times below the current ANSI maximum permissible exposure. RPE cell morphologies that were strikingly similar to those seen in postmortem histological studies were observed in AMD eyes, even in areas where the pattern of fluorescence appeared normal in commercial fundus autofluorescence (FAF) images. This new method can be used to study RPE morphology in AMD and other diseases, providing a powerful tool for understanding disease pathogenesis and progression, and offering a new means to assess the efficacy of treatments designed to restore RPE health.

J Opt Soc Am A Opt Image Sci Vis ; 30(12): 2595-604, 2013 Dec 01.
Artículo en Inglés | MEDLINE | ID: mdl-24323021


Adaptive optics (AO) imaging methods allow 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 and yielded better than 91% correspondence. 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.

Células Epiteliales/fisiología , Epitelio Pigmentado de la Retina/patología , Algoritmos , Animales , Automatización , Simulación por Computador , Fluorescencia , Humanos , Procesamiento de Imagen Asistido por Computador , Luz , Macaca , Microscopía Fluorescente/métodos , Distribución Normal , Oftalmoscopía/métodos , Óptica y Fotónica , Epitelio Pigmentado de la Retina/citología