Investigative Ophthalmology & Visual Science Cover Image for Volume 61, Issue 7
June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Meibomian gland visualization in external eye images, using a slit-scanning ophthalmoscope
Author Affiliations & Notes
  • Kevin Schwarz
    Carl Zeiss Meditec Inc., Dublin, California, United States
    Faculty of Natural Sciences and Technology, University of Applied Sciences and Arts Hildesheim/Holzminden/Goettingen (HAWK), Goettingen, Lower Saxony, Germany
  • Conor Leahy
    Carl Zeiss Meditec Inc., Dublin, California, United States
  • Angelina Covita
    Carl Zeiss Meditec Inc., Dublin, California, United States
  • Jochen Straub
    Carl Zeiss Meditec Inc., Dublin, California, United States
  • Footnotes
    Commercial Relationships   Kevin Schwarz, Carl Zeiss Meditec Inc. (C); Conor Leahy, Carl Zeiss Meditec Inc. (E); Angelina Covita, Carl Zeiss Meditec Inc. (E); Jochen Straub, Carl Zeiss Meditec Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 128. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Kevin Schwarz, Conor Leahy, Angelina Covita, Jochen Straub; Meibomian gland visualization in external eye images, using a slit-scanning ophthalmoscope. Invest. Ophthalmol. Vis. Sci. 2020;61(7):128.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : The visualization of meibomian glands (MG) is an important factor for clinical management of dry eye disease. In this study, we developed a dedicated scan pattern and image processing method for visualizing MG, using a slit-scanning ophthalmoscope.

Methods : Infrared (IR) images of the meibomian glands were captured using a slit-scanning ophthalmoscope CLARUS™ 500 (ZEISS, Dublin, CA) with prototype software. The patient’s eyelid had to be everted to capture the inner side of the lid. A modified scan pattern enabled alternation of the built-in IR light sources for reducing specular reflections, and a smaller scanning area aided in targeted acquisition of the MG.
For increasing the contrast between glands and background, direct imaging was combined with an offset-aperture technique (figure 1). Image processing algorithms were applied, including filters and contrast enhancement to improve the MG visibility.
Contrast to noise ratio (CNR)
CNR=│μGB│/(σGB)1/2
was measured from 4 glands (µGG) and adjacent background (µBB) areas with a size of 10x10-pixels, for each of seven images (example shown in Figure 2b) to determine the effect of the enhancement (a high value indicates that the contrast between gland and background is better than a lower value; figure 2 a).

Results : The box plot in figure 2a shows the CNR value as a computed average from seven images before and after contrast enhancement. The median CNR value increases from 60.5 to 101.3.

Conclusions : The offset-aperture technique was not useful in creating a usable image. However, a cropped image in combination with different contrast enhancement algorithm can increase MG visibility (figure 2c-e), potentially extending the capabilities of CLARUS 500 for use in dry eye management.

This is a 2020 ARVO Annual Meeting abstract.

 

Figure 1: Left normal imaging; Right offset-aperture imaging

Figure 1: Left normal imaging; Right offset-aperture imaging

 

Figure 2: (a) is a box plot with a total of 7 images of the CNR value calculated (before and after contrast enhancement). A significant increase in contrast can be seen after image processing. (b) shows the choosen areas in an example image for the CNR calculation. (c) shows the imaging result before any changes. (d) shows the cropped version, which the operator can see in the live view. (e) shows the result after improvement.

Figure 2: (a) is a box plot with a total of 7 images of the CNR value calculated (before and after contrast enhancement). A significant increase in contrast can be seen after image processing. (b) shows the choosen areas in an example image for the CNR calculation. (c) shows the imaging result before any changes. (d) shows the cropped version, which the operator can see in the live view. (e) shows the result after improvement.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×