A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images

Abstract

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropthy 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 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.

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

“…The sensitivity and specificity of a unified framework for detection of glaucomatous progression using CSLO images was reported as 86% and 88%, respectively. 85 …”

Macular imaging with optical coherence tomography in glaucoma

“…86,121 Several imaging tools were explored over the last 3 decades and found to be potentially useful for the diagnosis and management of glaucoma. 8,9,15,38,74,127,244,254,286,340,349,363 Optical coherence tomography (OCT) has become the structural imaging technology of choice for glaucoma diagnostics due to the speed of acquisition, high resolution, and excellent reproducibility. 88,342 It generates cross-sectional images of the posterior segment by measuring the echo time delay and the magnitude of backscattered light reflected off the retina or the ONH through the principle of lowcoherence interferometry.…”

Retinal Fundus Image for Glaucoma Detection: A Review and Study

“…This section presents glaucoma detection based on the various classifiers such as Fisher linear discriminant classifier, two-layer fuzzy classifier, KNN classifier, and AdaBoost classifier. Belghith et al [8] have developed the glaucoma progression detection method using Heidelberg retinal tomograph (HRT) images. Here, they have proposed a graphical model of MRF to handle the spatial pixel dependency.…”