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

Philippe Burlina, D. E. Freund, B. Dupas, Neil M Bressler

Research output: Contribution to journalArticle

Abstract

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%.

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Imagery (Psychotherapy)
Macular Degeneration
Pathology
Probability distributions
Screening
Classifiers
Tissue
Color
Normal Distribution
Blindness
Retina
Sensitivity and Specificity
Datasets

ASJC Scopus subject areas

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

Cite this

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title = "Automatic screening of age-related macular degeneration and retinal abnormalities.",
abstract = "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{\%}.",
author = "Philippe Burlina and Freund, {D. E.} and B. Dupas and Bressler, {Neil M}",
year = "2011",
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AU - Freund, D. E.

AU - Dupas, B.

AU - Bressler, Neil M

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AB - 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%.

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