Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: ρ=0.72±0.14; p≪0.05, glaucomatous: ρ=0.58±0.10; p≪0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.

Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Technicka 12 Brno 61600 Czech Republic

Ophthalmology Clinic M D Tomas Kubena U zimniho stadionu 1759 760 00 Zlin Czech Republic

Pattern Recognition Lab and Erlangen Graduate School of Advanced Optical Technologies University of Erlangen Nuremberg Martensstrase 3 91058 Erlangen Germany

St Anne Faculty Hospital Brno International Clinical Research Center Pekarska 53 65691 Brno Czech Republic; Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Technicka 12 Brno 61600 Czech Republic

University of Erlangen Nuremberg Department of Ophthalmology Schwabachanlage 6 91054 Erlangen Germany

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