April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Automated assessment of lid margin lissamine green staining
Author Affiliations & Notes
  • Carolina Kunnen
    Brien Holden Vision Institute, Vision CRC, Sydney, NSW, Australia
    University of New South Wales, Sydney, NSW, Australia
  • Percy Lazon De La Jara
    Brien Holden Vision Institute, Vision CRC, Sydney, NSW, Australia
    University of New South Wales, Sydney, NSW, Australia
  • Brien A Holden
    Brien Holden Vision Institute, Vision CRC, Sydney, NSW, Australia
    University of New South Wales, Sydney, NSW, Australia
  • Eric B Papas
    Brien Holden Vision Institute, Vision CRC, Sydney, NSW, Australia
    University of New South Wales, Sydney, NSW, Australia
  • Footnotes
    Commercial Relationships Carolina Kunnen, None; Percy Lazon De La Jara, None; Brien Holden, None; Eric Papas, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 1976. doi:https://doi.org/
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      Carolina Kunnen, Percy Lazon De La Jara, Brien A Holden, Eric B Papas; Automated assessment of lid margin lissamine green staining. Invest. Ophthalmol. Vis. Sci. 2014;55(13):1976. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

To develop and validate an objective, automated method for the assessment of lissamine green (LG) staining of the lid wiper region and establish objective quantifiable parameters which correspond with the judgement by human observers.

 
Methods
 

Upper eyelid margins of 27 participants were photographed after staining with LG. Images were processed using custom designed software developed in MATLAB. After manual delineation of the lid margin, images were transformed into a HSV format (hue, saturation, value). Spurious reflections were automatically eliminated prior to contrast enhancement, thresholding and binarisation. An example of an original image together with the automatically binarised outcome is shown in Figure 1A and B. The algorithm automatically extracts the following variables: proportion of LG staining relative to area of the eyelid (PA), median intensity of staining (IRGB), median intensity of staining in red channel (IR), green channel (IG) and blue channel (IR), relative greenness staining (RGS), green-red difference (GRD), green-blue difference (GBD) and median intensity of hue value (IH). Repeatability was assessed by capturing and measuring two images of the same 27 eyelids. Validity was tested by comparing the automated results to assessment of the 27 images made by ten observers, using a 0-3 grading scale.

 
Results
 

For PA, the mean difference between replicates was -0.06% with 95% limits of agreement being 1.07 and -1.13%. Correlation analysis showed significant associations (p < 0.05) between the mean grades by human observers and all parameters except for IRGB, GBD, IG and IB. PA showed the highest linear correlation with subjective grading (R2 = 0.63). The associations with the colorimetric parameters, GRD (R2 = 0.55), were less strong, but also showed a positive correlation with human assessment.

 
Conclusions
 

A semi-automatic system has been developed that permits objective, repeatable measures of lissamine green staining. Judgments of LG staining by human observers are not fully replicated by individual morphometric or colorimetric parameters.

 
 
Figure 1A: Original image with LG staining of upper eyelid
 
Figure 1A: Original image with LG staining of upper eyelid
 
 
Figure 1B: Enhanced, binarised image.
 
Figure 1B: Enhanced, binarised image.
 
Keywords: 526 eyelid • 549 image processing  
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