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Extraction of lens geometry in an optomechanical eye model during accommodation using automatic image processing
Author(s) -
GILLNER M,
EPPIG T,
KANNENGIESSER M,
LANGENBUCHER A
Publication year - 2012
Publication title -
acta ophthalmologica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.534
H-Index - 87
eISSN - 1755-3768
pISSN - 1755-375X
DOI - 10.1111/j.1755-3768.2012.s134.x
Subject(s) - accommodation , lens (geology) , extraction (chemistry) , artificial intelligence , computer vision , image processing , computer science , image (mathematics) , optics , optometry , medicine , physics , chromatography , chemistry
Purpose To extract curvature, lens thickness and optical performance of accommodative intraocular lenses (AIOL) in an optomechanical eye model. Methods Freshly enucleated porcine eyes with an implanted AIOL and removed cornea were placed in a cuvette with BSS (35°C) simulating physiological conditions. The ciliary body was expanded radially symmetric to stretch or release the zonula fibers effectuating pseudophakic accommodation. Dynamic accommodation and imaging quality was measured with a Shack‐Hartmann‐Sensor (SHS). Shape and relative lens position during accommodation was measured with an optical coherence tomograph (OCT). The 3D OCT image was processed with denoising and edge detection extracting the AIOL surface. Remaining haptic data were eliminated. Images were corrected by inverse ray‐tracing for compensation of the OCT’s beam divergence and refractive effects of optical surfaces. Finally the AIOL geometry was derived by a spherical fit and the center thickness was determined. Results The algorithm allows to extract edge data from noisy OCT images and up‐to‐scale geometrical data. Curvature was derived from a fit of the edge points and relative lens position, lens thickness as well as optical power for the optomechanical eye model were measured at different states of accommodation. Optics with a diameter up to 6 mm could be measured with a lateral/depth resolution of 20/5 µm. The results yield complimentary data to the image quality measured with the SHS. Conclusion The algorithms are elementary tools for the setup of the optomechanical eye model for AIOL. It is now possible to evaluate different AIOL in an experimental setup as a base for development of next generation AIOL.