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R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, G. Michelson; Multimodal Automated Glaucoma Detection Combining the Glaucoma Probability Score and the Glaucoma Risk Index. Invest. Ophthalmol. Vis. Sci. 2009;50(13):324.
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© ARVO (1962-2015); The Authors (2016-present)
Fundus camera and Heidelberg Retina Tomograph (HRT) are commonly used for reliable glaucoma diagnosis. Quantitative glaucoma scores, however, do not utilize both image content simultaneously. We propose the combination of topography and fundus image based indices for automated glaucoma detection which outperforms their sole application of either.
The probabilistic values of topography based Glaucoma Probability Score (GPS) and our fundus image based Glaucoma Risk Index (GRI) are assembled to a two-dimensional feature space. In contrast to established methods the subsequent application of a probabilistic nu-Support Vector Machine classifier (nu = 0.5, kernel: radial basis function) uses both the topographic and the textural information to determine a final glaucoma probability. Instances labeled with a final probability greater than 0.5 are considered glaucomatous.For the evaluations in a 10-fold cross-validation setup, we took a sample set (mean age: 55.4 ± 10.9 years) of papilla images of 149 glaucomatous patients (FDT test time 67.4 ± 35.6 s) and 246 normals from the Erlangen Glaucoma Registry. The gold standard diagnosis was given by a glaucoma specialist based on an elaborate ophthalmological examination with ophthalmoscopy, visual field, IOP, FDT, and HRT II. The GPS was calculated by HRT device while papilla centered color fundus images (Kowa non-myd, FOV 22°) were used to calculate the GRI.
The classification of the GRI resulted in an area under ROC curve (AUC) of 0.81 with an F-measure of 0.71 for glaucomatous cases and 0.83 for normals. The GPS achieved an AUC of 0.86 while the F-measure for glaucoma was 0.74 (F-measure for healthy was 0.84).The combination of both indices clearly increased the AUC by 4% up to 0.9 compared to the sole application of the GPS. The F-measure for glaucomatous images was improved up to 0.76 (F-measure for healthy images was 0.86).
The proposed combination of the topography based GPS and the fundus image based GRI shows superior performance compared to either index alone.Both indices utilize complementary information about the glaucoma disease. Consequently, this multimodal combined application of both indices is promising to reach a more reliable automated glaucoma detection performance. The approach can be used in large screening applications where an automated tool is essential to support the experts in finding glaucomatous eyes.
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