Glaucoma risk index: Automated glaucoma detection from color fundus images

Rüdiger Bock, Jörg Meier, László G. Nyúl, Joachim Hornegger, Georg Michelson

Research output: Contribution to journalArticle

203 Citations (Scopus)

Abstract

Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head.

Original languageEnglish
Pages (from-to)471-481
Number of pages11
JournalMedical Image Analysis
Volume14
Issue number3
DOIs
Publication statusPublished - Jun 1 2010

Keywords

  • Appearance-based image analysis
  • Computer aided diagnosis
  • Glaucoma
  • Linear principal component analysis
  • Optic disk

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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