Odstrcilik Jan, Kolar Radim, Tornow Ralf-Peter, Jan Jiri, Budai Attila, Mayer Markus, Vodakova Martina, Laemmer Robert, Lamos Martin, Kuna Zdenek, Gazarek Jiri, Kubena Tomas, Cernosek Pavel, Ronzhina Marina
St. Anne Faculty Hospital Brno - International Clinical Research Center (ICRC), 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.
St. Anne Faculty Hospital Brno - International Clinical Research Center (ICRC), 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.
Comput Med Imaging Graph. 2014 Sep;38(6):508-16. doi: 10.1016/j.compmedimag.2014.05.005. Epub 2014 May 21.
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.
眼底图像在眼科中被常规使用。由于使用眼底相机进行检查是一种相对快速且廉价的程序，它可以用作筛查视网膜疾病（如青光眼）的合适诊断工具。青光眼的症状之一是视网膜神经纤维层（RNFL）的渐进性萎缩，导致RNFL厚度的变化。在此，我们介绍一种新颖的方法，通过对标准且易于获取的彩色眼底图像中RNFL纹理外观进行计算机辅助分析来捕捉这些变化。所提出的方法使用基于高斯马尔可夫随机场和局部二值模式的特征，以及各种回归模型来预测RNFL厚度。该方法能够描述RNFL纹理的变化，直接反映RNFL厚度的变化。在16只正常（“健康”）眼睛和8只青光眼眼睛上对该方法进行了评估。我们在模型预测输出值与通过光学相干断层扫描测量的RNFL厚度之间取得了显著相关性（正常眼睛：ρ = 0.72±0.14；p≪0.05，青光眼眼睛：ρ = 0.58±0.10；p≪0.05），光学相干断层扫描目前被视为标准的青光眼评估设备。因此，评估表明所提出的方法在测量可能的RNFL变薄方面具有良好的适用性。
