Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration

David J. Ramsey, Janet S. Sunness, Poorva Malviya, Carol Applegate, Gregory D. Hager, James T. Handa

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1296-1307
Number of pages12
JournalRetina
Volume34
Issue number7
DOIs
StatePublished - Jul 2014

Keywords

  • age-related macular degeneration
  • automated image segmentation
  • fundus autofluorescence
  • geographic atrophy

ASJC Scopus subject areas

  • Ophthalmology

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