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Abstract #25932 Published in IGR 12-2
Glaucoma risk index: Automated glaucoma detection from color fundus images
Bock R; Meier J; Nyul LG; Hornegger J; Michelson GMedical Image Analysis 2010; 14: 471-481
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.
R. Bock. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany. ruediger.bock@informatik.uni-erlangen.de
Classification:
6.8.2 Posterior segment (Part of: 6 Clinical examination methods > 6.8 Photography)
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
1.6 Prevention and screening (Part of: 1 General aspects)
