March 2012
Volume 53, Issue 14
Free
ARVO Annual Meeting Abstract  |   March 2012
Quantification Of Autofluorescence Using A Pixel Mapping Technique In Different Stages Of Dry Age-related Macular Degeneration
Author Affiliations & Notes
  • Ira H. Schachar
    Ophthalmology, University of Michigan, Ann Arbor, Michigan
  • Sarwar Zahid
    Ophthalmology, University of Michigan, Ann Arbor, Michigan
  • Michelle Cote
    Ophthalmology, University of Michigan, Ann Arbor, Michigan
  • Anthony Leithauser
    Ophthalmology, University of Michigan, Ann Arbor, Michigan
  • K Thiran Jayasundera
    Ophthalmology, University of Michigan, Ann Arbor, Michigan
  • Victor M. Elner
    Ophthalmology, University of Michigan, Ann Arbor, Michigan
  • Footnotes
    Commercial Relationships  Ira H. Schachar, None; Sarwar Zahid, None; Michelle Cote, None; Anthony Leithauser, None; K Thiran Jayasundera, None; Victor M. Elner, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 3110. doi:
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      Ira H. Schachar, Sarwar Zahid, Michelle Cote, Anthony Leithauser, K Thiran Jayasundera, Victor M. Elner; Quantification Of Autofluorescence Using A Pixel Mapping Technique In Different Stages Of Dry Age-related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2012;53(14):3110.

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

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Abstract
 
Purpose:
 

To quantify changes in autofluorescence (AF) occurring in varying stages of dry age-related macular degeneration (AMD) using a pixel mapping technique.

 
Methods:
 

The Heidelberg Spectralis OCT (Heidelberg Engineering, Heidelberg, Germany) was used to capture AF images of controls and patients with dry-AMD, spanning all stages of clinical disease. The patients were divided into categories of disease severity by the AREDS grading system. The AF pixel mean was calculated for vertical columns to create a two-dimensional representation of each patient’s AF waveform through the fovea. Differences between the control waveforms and disease waveforms were quantified using the sum of a point-by-point difference squared technique, which provided a total deviation. Because of variations in image contrast, each waveform was adjusted by a constant to minimize pattern deviations and the temporal macula was excluded to aid in waveform matching.

 
Results:
 

Pattern deviation among controls showed a variation in AF pixel waveforms of < 10%. Pattern deviation (PD) from the control waveforms correctly identified mild AMD (10% < PD < 40%), moderate AMD (40% < PD < 70%) and severe AMD (PD > 70%) when the AREDS clinical grading score was used as the gold standard. Different patterns within the same clinical severity category were identified using the pixel mapping technique (see figure 1).

 
Conclusions:
 

The AF pixel mapping technique offers a unique objective method of quantifying severity in dry AMD and identifying subtle changes between patients having similar clinical scores. This technique represents the first quantification of AF images and will have direct applications in monitoring disease progression in diseases whose AF signatures change over time such as dry AMD.  

 
Keywords: imaging/image analysis: clinical • retina 
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