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

https://doi.org/10.1016/j.compmedimag.2014.05.005Get rights and content
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Highlights

  • A texture analysis approach for assessment of the retinal nerve fiber layer thickness in color fundus images.

  • Suitable for computer-aided diagnosis of glaucoma.

  • Advanced texture analysis methods – local binary patterns and Gaussian Markov random fields.

  • Results compared to measurements provided by optical coherence tomography.

Abstract

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.

Keywords

Glaucoma
Retinal nerve fiber layer
Texture analysis
Fundus images
Local binary patterns
Markov random fields

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