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Ramsey, David J. MD, PhD, MPH*; Sunness, Janet S. MD; Malviya, Poorva MA; Applegate, Carol BS; Hager, Gregory D. PhD; Handa, James T. MD*

doi: 10.1097/IAE.0000000000000069
Original Study

Purpose: To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA).

Methods: The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients.

Results: The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs.

Conclusion: The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

Progression of geographic atrophy associated with age-related macular degeneration is often monitored using fundus autofluorescence imaging. Quantifying atrophy in photographs differs between graders and imaging modalities. The purpose of this investigation is to develop a computer-based image segmentation method as a means of standardizing the quantification of geographic atrophy.Supplemental Digital Content is Available in the Text.

*Wilmer Eye Institute, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland;

Hoover Rehabilitation Low Vision Services, Greater Baltimore Medical Center, Baltimore, Maryland; and

Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.

Reprint requests: James T. Handa, MD, Room 3015, Smith Building, 400 North Broadway, Baltimore, MD 21287; e-mail:

Supported in part by the National Institutes of Health (EY08552 JSS), Research to Prevent Blindness (Wilmer), and Johns Hopkins Internal funds.

None of the authors have any financial/conflicting interests to disclose.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

J. T. Handa is the Robert Bond Welch Professor.

© 2014 by Ophthalmic Communications Society, Inc.