May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
An Improved Shack-Hartmann Centroid Detection Method for Wavefront Measurement and Correction With Adaptive Optics
Author Affiliations & Notes
  • J. M. Wanek
    Ophthalmology and Visual Sciences, Univ of Illinois at Chicago, Chicago, Illinois
  • M. Shahidi
    Ophthalmology and Visual Sciences, Univ of Illinois at Chicago, Chicago, Illinois
  • M. Mori
    Ophthalmology and Visual Sciences, Univ of Illinois at Chicago, Chicago, Illinois
  • Footnotes
    Commercial Relationships  J.M. Wanek, None; M. Shahidi, None; M. Mori, None.
  • Footnotes
    Support  NEI
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 4194. doi:https://doi.org/
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    • Get Citation

      J. M. Wanek, M. Shahidi, M. Mori; An Improved Shack-Hartmann Centroid Detection Method for Wavefront Measurement and Correction With Adaptive Optics. Invest. Ophthalmol. Vis. Sci. 2008;49(13):4194. doi: https://doi.org/.

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

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Abstract

Purpose: : Accurate detection of centroid locations on Shack-Hartmann (SH) images is a vital component of wavefront sensing and correction with adaptive optics (AO). A modified diminished area of interest (pyramidal) technique was developed for centroid detection with improved speed and accuracy as compared to conventional methods.

Methods: : SH images were acquired using an adaptive optics ophthalmoscope consisting of a 780 nm laser diode and SH sensor for wavefront measurement, and a deformable mirror for wavefront correction. Successive SH images were recorded in a model eye (108 spots, 150 images) and human eye (134 spots, 50 images) with no AO correction. SH images were also captured during AO correction (20 images). A modified pyramidal algorithm was developed to use centroid locations from a prior SH image as the starting location for centroid search areas in the next acquired SH image, thereby allowing smaller search windows and fewer recursive size reductions. Accuracy was evaluated by calculating the variance of centroid locations and the standard deviation (SD) of the root mean squared (RMS) wavefront error. To evaluate speed, the time to compute centroid locations of 108 spots in 150 SH images was recorded. Algorithm performance during AO correction was assessed by measuring the uncorrected and corrected RMS wavefront error in a model and human eye. The modified pyramidal technique accuracy and speed was compared to the center of mass and conventional pyramidal algorithms.

Results: : In a model eye, variance of centroid locations for the modified pyramidal technique was 0.0389 pixel, lower than the center of mass (0.594 pixel) and conventional pyramidal methods (0.0718 pixel). The RMS wavefront error SD was less with the modified pyramidal algorithm than the center of mass and pyramidal. In a human eye, the modified pyramidal technique centroid variance (1.284 pixel) was lower than the center of mass (3.173 pixel) and comparable to the conventional pyramidal (1.245 pixel). The RMS wavefront error SD with the modified pyramidal method was less than the center of mass and similar to the pyramidal. The modified pyramidal technique was 2.4 times faster than the conventional pyramidal method and comparable to the center of mass algorithm. AO correction performance in the model eye and human eye was similar with all 3 centroid detection algorithms.

Conclusions: : By modifying the pyramidal centroid detection algorithm, accuracy and speed of centroid measurements were improved compared to conventional centroid techniques, thereby allowing accurate wavefront measurements and faster AO correction.

Keywords: imaging/image analysis: non-clinical • aberrations • image processing 
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