June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Masking Vasculature and Measuring Fundus Autofluorescence using Standard Grids
Author Affiliations & Notes
  • Kenneth R Sloan
    Computer and Information Science, UAB, Birmingham, AL
    Department of Ophthalmology, UAB, Birmingham, AL
  • Fazila Aseem
    Department of Ophthalmology, UAB, Birmingham, AL
  • Anna V Zarubina
    Department of Ophthalmology, UAB, Birmingham, AL
  • Mark E Clark
    Department of Ophthalmology, UAB, Birmingham, AL
  • Cynthia Owsley
    Department of Ophthalmology, UAB, Birmingham, AL
  • Christine A Curcio
    Department of Ophthalmology, UAB, Birmingham, AL
  • Footnotes
    Commercial Relationships Kenneth Sloan, None; Fazila Aseem, None; Anna Zarubina, None; Mark Clark, None; Cynthia Owsley, None; Christine Curcio, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 5267. doi:
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    • Get Citation

      Kenneth R Sloan, Fazila Aseem, Anna V Zarubina, Mark E Clark, Cynthia Owsley, Christine A Curcio; Masking Vasculature and Measuring Fundus Autofluorescence using Standard Grids. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):5267.

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

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Abstract
 
Purpose
 

Create a semi-automatic workflow to establish a frame of reference for standard grids (e.g., ETDRS), identify regions for AF analysis, and visualize results.

 
Methods
 

A custom FIJI plug-in segmented Spectralis AF images and guided a trained observer in editing a mask excluding vessels. We selected the Phansalkar adaptive local thresholding method (a standard Fiji tool) applied over circular regions of r= 200 pixels. Images were registered using the foveal center and scale information from the instrument. Stand-alone Java programs accepted the original AF image, the mask, and scale and location information and tabulated AF intensities for unmasked pixels grouped by regions within standard grids. Statistical properties including texture measures were computed for grid regions and presented as tables and as custom visualizations.

 
Results
 

A total of 660 images (1536x1536 8-bit grayscale) from the Alabama Study on Age-Related Macular Degeneration were processed. The Phansalkar method produced a satisfactory initial segmentation in a few seconds per image. Manual editing required 10-30 minutes per image, depending on image complexity and experience of the editor. The final segmentations were judged to be very good to excellent.

 
Conclusions
 

Standard Fiji tools can segment retinal vasculature and other excluded features when augmented by a final manual editing. Masking the vasculature and other features is superior to histogram-based methods, and extends AF measurement in retinal images to a broader field of view.  

 
Left: AF image of macula with grid overlay; sampled pixels are green, masked pixels are red.<br /> Middle: display of mean (top), coefficient of variation (middle), and a composite using hue and intensity to show both.<br /> Right: histograms of sampled pixel intensities in each region.
 
Left: AF image of macula with grid overlay; sampled pixels are green, masked pixels are red.<br /> Middle: display of mean (top), coefficient of variation (middle), and a composite using hue and intensity to show both.<br /> Right: histograms of sampled pixel intensities in each region.

 
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