Belghith Akram, Balasubramanian Madhusudhanan, Bowd Christopher, Weinreb Robert N, Zangwill Linda M
Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA, United States.
Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA, United States; Department of Electrical & Computer Engineering, The University of Memphis, Memphis, TN, United States; Department of Biomedical Engineering, The University of Memphis, Memphis, TN, United States; Department of Biomedical Engineering, University of Tennessee Health Science Center, Memphis, TN, United States.
Comput Med Imaging Graph. 2014 Jul;38(5):411-20. doi: 10.1016/j.compmedimag.2014.03.002. Epub 2014 Mar 13.
Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection.
青光眼是全球第二大致盲原因，是一种以视神经乳头（ONH）和视野的独特变化为特征的视神经病变。青光眼进展的检测是原发性开角型青光眼（OAG）管理中最重要且最具挑战性的方面之一。在此背景下，眼部成像设备日益精密，提供了定量工具来测量ONH地形的结构变化，这是确定疾病是否恶化的关键要素。特别是，海德堡视网膜断层扫描仪（HRT），一种共焦扫描激光技术，已被广泛用于检测青光眼并监测其进展。在本文中，我们提出了一种使用HRT图像检测青光眼进展的新框架。与之前未整合图像中可用的先验知识，特别是变化检测图中的空间像素依赖性的工作不同，我们提出使用马尔可夫随机场来处理这种依赖性。据我们所知，这是变分期望最大化（VEM）算法在青光眼进展检测框架中用于推断ONH地形变化的首次应用。将所提出框架的诊断性能与最近提出的进展检测方法进行了比较。
