Automatic screening of age-related macular degeneration and retinal abnormalities.

P. Burlina, D. E. Freund, B. Dupas, N. Bressler

Research output: Contribution to journalArticlepeer-review


We describe a novel approach for screening retinal imagery to detect evidence of abnormalities. In this paper, we focus our efforts on age-related macular degeneration (AMD), a pathology that may often go undetected in the early or intermediate stages, and can lead to a neovascular form often resulting in blindness, if untreated. Our strategy for retinal anomaly detection is to employ a single class classifier applied to fundus imagery. We use a multiresolution locally-adaptive scheme that identifies both normal and anomalous regions within the retina. We do this by using a hybrid parametric/non-parametric characterization of the support of the probability distribution of normal retinal tissue in color and intensity feature space. We apply this approach to screen for evidence of AMD on a dataset of 66 healthy and pathological cases and found a detection sensitivity and specificity of 95% and 96%.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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