April 2014
Volume 55, Issue 13
ARVO Annual Meeting Abstract  |   April 2014
A Novel Method for Interpretation and Visualization of Spatial Lipid Layer Thickness Data
Author Affiliations & Notes
  • Jesse Winans
    TearScience, Morrisville, NC
  • Nathan Luck
    TearScience, Morrisville, NC
  • Scott Liddle
    TearScience, Morrisville, NC
  • Stephen Grenon
    TearScience, Morrisville, NC
  • Footnotes
    Commercial Relationships Jesse Winans, TearScience (E); Nathan Luck, TearScience (E); Scott Liddle, TearScience (E); Stephen Grenon, TearScience (E)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4846. doi:
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    • Get Citation

      Jesse Winans, Nathan Luck, Scott Liddle, Stephen Grenon; A Novel Method for Interpretation and Visualization of Spatial Lipid Layer Thickness Data. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4846.

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

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Purpose: To develop a software data algorithm and visualization mode for the LipiView Ocular Surface Interferometer that tracks maximum lipid thickness at each location on the tear film during an interblink period providing a visual metric of maximum lipid coverage.

Methods: The LipiView captures video of the ocular surface and isolates the interferometric colors produced by reflection off the tear film. A validated algorithm translates the isolated color frames into lipid layer thickness (LLT) on a pixel-by-pixel basis. The new “peak detect” algorithm begins with the first frame of an interblink period, and creates a new peak frame, setting the LLT of each pixel to its corresponding value in the first frame. The LLT values in the second frame are then compared to their corresponding values in the peak frame. Each pixel in the peak frame is set to the larger of its peak frame value and its value in the second frame of the interblink period. This process of retrospectively recording the highest LLT for every pixel proceeds until the end of the interblink period. The final peak frame of the interblink period shows the thickest lipid layer attained for every pixel in the illumination pattern between blinks. The peak frame is reset at the beginning of the next interblink period and the algorithm continues until a peak frame has been generated for every frame in the original video.

Results: The peak detect algorithm allows for improved visualization of lipid flow dynamics, and provides a method for identifying regions of the ocular surface that do not receive adequate or uniform lipid coverage during the blink cycle. The final peak frame of an interblink period captures the motion of the lipid layer throughout an interblink period in a single static image.

Conclusions: Peak detection provides a method for accessing lipid coverage and readily detecting irregularities in lipid layer distribution, which may be indicative of Meibomian gland disease or ocular surface abnormalities. It can provide a single static image which represents maximum lipid coverage during the interblink period.

Keywords: 549 image processing • 583 lipids • 550 imaging/image analysis: clinical  

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