June 2023
Volume 64, Issue 9
Open Access
ARVO Imaging in the Eye Conference Abstract  |   June 2023
Automatic corneal tracking algorithm for in-vivo Brillouin microscopy
Author Affiliations & Notes
  • Hongyuan Zhang
    Cleveland Clinic, Cleveland, Ohio, United States
  • Giuliano Scarcelli
    University of Maryland at College Park, College Park, Maryland, United States
  • James Bradley Randleman
    Cleveland Clinic, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Hongyuan Zhang, None; Giuliano Scarcelli, None; James Randleman, None
  • Footnotes
    Support  NIH Grant R01 EY028666, Unrestricted Grants from The Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2023, Vol.64, PB0011. doi:
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    • Get Citation

      Hongyuan Zhang, Giuliano Scarcelli, James Bradley Randleman; Automatic corneal tracking algorithm for in-vivo Brillouin microscopy. Invest. Ophthalmol. Vis. Sci. 2023;64(9):PB0011.

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

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Abstract

Purpose : Reduce the peak-search error caused by intensity fluctuation of the anterior corneal surface when using OCT to locate corneal position in a Brillouin microscope.

Methods : A whole OCT spectrum, instead of the sole anterior peak, was used in corneal position determination to make the tracking algorithm less sensitive to intensity fluctuations. To involve the whole spectrum, convolution between OCT spectra was calculated to find the maximum similarity and the corresponding distance shift. The accuracy of this convolution algorithm was tested by tracking the movement of a pig eye mounted on a translational stage, moving 1 mm with a step size of 0.1 mm. Moreover, the distance shifts from an in-vivo dataset were calculated by anterior peak search, convolution, and manual determination separately to compare errors between auto-recognition and manual selection.

Results : In the pig eye tracking, errors from the convolution algorithm stayed within 3 μm over the 1 mm range. When compared to the manual selection, the maximum error from the convolution algorithm was 17 μm. In contrast, peak search algorithm led to an error as large as 100 μm.

Conclusions : By introducing the whole OCT spectrum in position tracking, the proposed convolution algorithm makes distance deciphering less sensitive to the intensity of the anterior peak while maintaining high tracking accuracy. This developed algorithm improves time efficiency of post processing, making the processing more automated.

This abstract was presented at the 2023 ARVO Imaging in the Eye Conference, held in New Orleans, LA, April 21-22, 2023.

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