June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Higher order statistical eye model for normal eyes
Author Affiliations & Notes
  • Jos J Rozema
    Ophthalmology, Antwerp University Hospital, Edegem, Belgium
    Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
  • Pablo Rodriguez Perez
    Facultad de Ciencias, ICMA Consejo Superior de Investigaciones Científicas-Universidad de Zaragoza, Zaragoza, Spain
  • Rafael Navarro
    Facultad de Ciencias, ICMA Consejo Superior de Investigaciones Científicas-Universidad de Zaragoza, Zaragoza, Spain
  • Marie-Jose B R Tassignon
    Ophthalmology, Antwerp University Hospital, Edegem, Belgium
    Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
  • Footnotes
    Commercial Relationships Jos Rozema, None; Pablo Rodriguez Perez, None; Rafael Navarro, None; Marie-Jose Tassignon, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 6020. doi:
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    • Get Citation

      Jos J Rozema, Pablo Rodriguez Perez, Rafael Navarro, Marie-Jose B R Tassignon; Higher order statistical eye model for normal eyes. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):6020.

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

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Abstract

Purpose: This work presents a stochastic model capable of generating an unlimited number of random, but realistic biometry sets, including the corneal elevation, intraocular distances and wavefronts, with the same statistical and epidemiological properties as the original data it is based on.

Methods: One cohort of 312 eyes of 312 healthy Caucasian subjects (aged 20 - 60 years) was measured with an autorefractometer, Scheimpflug imaging (Oculus Pentacam), optical biometer (Haag-Streit Lenstar) and an aberrometer (Tracey iTrace). The corneal elevation maps, represented by Zernike coefficients, were compressed using Principal Component Analysis, leaving a total of 17 parameters to describe the variability of the ocular biometry. These data were then fitted with a linear combination of three multivariate Gaussians through an Expectation Maximization algorithm, which has been shown to give a good representation of the ocular biometry in a population. Based on this fit a stochastic model was built that generates an unlimited number of random biometry sets, from which total wavefronts and other ocular parameters can be calculated. Equality between the original and the synthetic data was assessed using non-parametric "two one-sided" tests.

Results: The wavefronts calculated using the measured biometry were significantly equal to the originally measured wavefronts (two one-sided Wilcoxon test, p < 0.05), confirming the accuracy of the ray tracing algorithm. Subsequently, the stochastic model was used to randomly generate the biometry of 1000 eyes and calculate the associated wavefronts by ray tracing. For both the biometry and the wavefront this synthetic data were significantly equal to the originally measured data (two one-sided Mann-Witney test, p < 0.05), thus making them statistically indistinguishable.

Conclusions: The statistical eye model is able to produce synthetic biometry data that is indistinguishable from actual biometry. As such this model may be an interesting alternative to static eye models for researchers in visual optics that do not have access to biometry data.

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