April 2011
Volume 52, Issue 14
ARVO Annual Meeting Abstract  |   April 2011
Examining FFT and Direct Counting Estimates of Photoreceptor Density in Adaptive Optics Retinal Images
Author Affiliations & Notes
  • Robert F. Cooper
    Biomedical Engineering, Marquette University, Milwaukee, Wisconsin
  • Jungtae Rha
    Medical College of Wisconsin, Milwaukee, Wisconsin
  • Alfredo Dubra
    Flaum Eye Institute, University of Rochester, Rochester, New York
  • Joseph Carroll
    Cell Biology, Neurobiology, & Anatomy,
    Medical College of Wisconsin, Milwaukee, Wisconsin
  • Footnotes
    Commercial Relationships  Robert F. Cooper, None; Jungtae Rha, None; Alfredo Dubra, None; Joseph Carroll, None
  • Footnotes
    Support  Research to Prevent Blindness, NIH grants P30EY001931 & R01EY017607. AD holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 1321. doi:
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      Robert F. Cooper, Jungtae Rha, Alfredo Dubra, Joseph Carroll; Examining FFT and Direct Counting Estimates of Photoreceptor Density in Adaptive Optics Retinal Images. Invest. Ophthalmol. Vis. Sci. 2011;52(14):1321.

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

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Purpose: : Adaptive optics (AO) imaging systems permit direct visualization of the photoreceptor mosaic. One of the more common metrics derived from these images is density, however, in cases where the mosaic is incomplete, either due to poor image quality, pathology, or interruption by another cell type, FFT-estimates of density can be inaccurate. Here we sought to analyze the efficacy of FFT techniques in deriving accurate estimates of density in AO images of varying density and disruption.

Methods: : Eleven subjects with normal vision and five subjects with retinal pathology were imaged using an AO ophthalmoscope and/or an AO scanning laser ophthalmoscope. Photoreceptor density was calculated in all 16 images using manual and semi-automated direct counting methods as well as from the radial average of the FFT. Synthetic mosaics were constructed using photoreceptor coordinates from 10 of the normal cone mosaic images. This allowed us to examine the effect of system noise and background artifacts on the FFT, and also to examine the effect of mosaic disruption by randomly removing varying amounts of cones from the synthetic mosaic (10-70%).

Results: : With the original cone mosaic images, we found that FFT-derived estimates of density matched those from the synthetic images within 5%, validating the use of the synthetic images for further modeling. Compared to both of these FFT-derived estimates, the direct count density was 12% greater on average. Furthermore, FFT-derived estimates of density degraded in some synthetic images when as few as 30% of the cones were randomly removed. This discrepancy was also seen in the pathological mosaics. In images containing both rods and cones, 2 peaks are visible in the radial average of the FFT, however the FFT-derived estimate of rod density overestimates the direct count density by over 50%. Correcting for the area of the image occupied by interleaved cones results in agreement within 5% between the FFT-derived and direct-count density estimates.

Conclusions: : The source of the discrepancy between FFT-derived and direct-count cone density estimates remains to be determined, though likely results from assumptions made in converting peak spatial frequency to a density value. As AO systems can now image rods and cones simultaneously, it is important to develop methods to reliably retrieve density estimates of both mosaics, without resorting to counting every cell, especially as 100% of the cells may not be distinguishable.

Keywords: image processing • photoreceptors • imaging/image analysis: non-clinical 

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