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J. Meier, R. Bock, C. Forman, L. G. Nyúl, J. Hornegger, G. Michelson; The Erlanger Glaucoma Matrix - A Visualization Approach Towards Optimal Glaucomatous Optic Nerve Head Image Presentation. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1893.
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© ARVO (1962-2015); The Authors (2016-present)
Presentation of a two-dimensional visualization approach for intuitive and reliable glaucoma diagnosis and for setting a current observation into a relationship with pre-diagnosed data.
We present a new matrix visualization technique for digital optic nerve head images. The matrix is filled with 300 pre-diagnosed reference images which show different papilla sizes and varying stages of glaucoma disease. In matrix rows the samples range from healthy ones to advanced glaucoma cases. In matrix columns the papillas are ordered by the size of the optic nerve head. The approach generalizes such that the samples can be ordered by additional criteria, too, e. g. subjects' age or anamnestic risk factors. Furthermore arbitrary image modalities and image numbers can be incorporated.
The glaucoma classification of a single image is difficult even for experts. Our proposed visualization provides an intuitive way for neighborhood comparisons of optic nerve head images. It allows to evaluate an image in the context of given pre-diagnosed reference samples. By the two-dimensional presentation one can study disease-dependent changes separate from other variations. Glaucoma progression can be observed separated from size variations. Thus, it supports diagnosis even in problematic cases such as macropapillas. The trustworthiness of physicians' diagnosis can be improved.
Our approach gives insights on glaucomatous optic nerve appearance in relation to varying papilla sizes. The novel visualization of a single image within the context of other images is considered as an important tool for learning and training medical glaucoma detection. This approach visualizes computer calculated risk estimations by presenting the result within context of given gold-standard images. In contrast to pure classification systems our method does not come up with a hard decision but explains the relationship to similar pre-diagnosed cases.
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