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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)
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.
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.
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.
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.
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