Thickness related textural properties of retinal nerve fiber layer in color fundus images

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: R=0.72, p<0.05, glaucomatous: R=0.58, 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.
Článek se zabývá texturní analýzou fundus fotografií pomocí texturní analýzy za účelem odhadu tloušťky vrstvy nervových vláken.

Keywords

Glaucoma, Retinal nerve fiber layer, Texture analysis, Fundus images, Local binary patterns, Markov random fields, glaukom; vrstva nervových vláken, texturní analýza, snímky sítnice, lokální binární vzory, Markovovy náhodná pole

Citation

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS. 2014, vol. 38, issue 6, p. 508-516.
https://www.sciencedirect.com/science/article/pii/S0895611114000652?via%3Dihub

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported
http://creativecommons.org/licenses/by-nc-nd/3.0/