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Corneal wave aberration from videokeratography: accuracy and limitations of the procedure

Guirao, Antonio, Artal, Pablo·2000·Journal of the Optical Society of America A

Abstract

A procedure to calculate the wave aberration of the human cornea from its surface shape measured by videokeratography is presented. The wave aberration was calculated as the difference in optical path between the marginal rays and the chief ray refracted at the surface, for both on- and off-axis objects. The corneal shape elevation map was obtained from videokeratography and fitted to a Zernike polynomial expansion through a Gram-Schmidt orthogonalization. The wave aberration was obtained also as a Zernike polynomial representation. The accuracy of the procedure was analyzed. For calibrated reference surface elevations, a root-mean-square error (RMSE) of 1 to 2 microm for an aperture 4-6 mm in diameter was obtained, and the RMSE associated with the experimental errors and with the fitting method was 0.2 microm. The procedure permits estimation of the corneal wave aberration from videokeratoscopic data with an accuracy of 0.05-0.2 microm for a pupil 4-6 mm in diameter, rendering the method adequate for many applications.

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Metadata

Authors
Guirao, A., & Artal, P.
Venue
Journal of the Optical Society of America A
Published
2000-06-01
Volume
17
Issue
6
Pages
955
ISSN
  • 1084-7529 (ISSN-L)
  • 1520-8532
OpenAlex
W1974535269
Cited by
172
References
0
FWCI
6.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Corneal wave aberration from videokeratography: accuracy and limitations of the procedure. https://app.cochat.ai/papers/10.1364/josaa.17.000955
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Guirao, A., & Artal, P. (2000). Corneal wave aberration from videokeratography: accuracy and limitations of the procedure. Journal of the Optical Society of America A. https://doi.org/10.1364/josaa.17.000955

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.