Belghith Akram, Bowd Christopher, Weinreb Robert N, Zangwill Linda M
Hamilton Glaucoma Center, University of California San Diego, La Jolla, California.
Proc SPIE Int Soc Opt Eng. 2014 Mar 18;9035:90350O. doi: 10.1117/12.2041980.
Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.
青光眼是一种眼部疾病，其特征是视神经乳头（ONH）和视野发生明显变化。青光眼可能在没有症状的情况下发作，如果不进行治疗会导致失明。因此，早期疾病检测很重要，以便能够开始治疗并预防失明。在这种背景下，眼部非侵入性成像技术取得了重要进展，提供了测量ONH地形结构变化的定量工具，这是青光眼检测和监测的关键要素。三维光谱域光学相干断层扫描（SD-OCT）是一种光学成像技术，已被广泛用于区分青光眼患者和健康受试者。在本文中，我们提出了一种使用三维SD-OCT图像检测青光眼进展的新框架。与以往利用商用光谱域光学相干断层扫描仪提供的视网膜神经纤维层（RNFL）厚度测量结果的研究不同，我们考虑整个三维体积进行变化检测。为了整合先验知识，特别是变化检测图中的空间体素依赖性，我们建议使用马尔可夫随机场来处理这种依赖性。为了处理误报检测的问题，然后基于模糊逻辑分类器，利用估计的变化检测图将三维SD-OCT图像分类为“非进展性”和“进展性”青光眼类别。我们将所提出框架的诊断性能与现有的进展检测方法进行了比较。
