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Comput Med Imaging Graph. 2014 Jul;38(5):411-20. doi: 10.1016/j.compmedimag.2014.03.002. Epub 2014 Mar 13.

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

Author information

1
Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA, United States. Electronic address: abelghith@ucsd.edu.
2
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. Electronic address: bmadhu@ieee.org.
3
Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA, United States. Electronic address: cbowd@ucsd.edu.
4
Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA, United States. Electronic address: rweinreb@ucsd.edu.
5
Hamilton Glaucoma Center, University of California San Diego, La Jolla, CA, United States. Electronic address: lzangwill@ucsd.edu.

Abstract

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.

KEYWORDS:

Change detection; Glaucoma; Markov field; Variational approximation

PMID:
24709053
PMCID:
PMC4053521
DOI:
10.1016/j.compmedimag.2014.03.002
[Indexed for MEDLINE]
Free PMC Article

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