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J Med Imaging (Bellingham). 2014 Oct;1(3):034504. doi: 10.1117/1.JMI.1.3.034504. Epub 2014 Dec 29.

Glaucoma progression detection using nonlocal Markov random field prior.

Author information

1
University of California San Diego , Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, California 92093-0946, United States.
2
University of Memphis , Department of Electrical and Computer Engineering, 3815 Central Avenue, Memphis, Tennessee 38152 United States ; University of Memphis , Department of Biomedical Engineering, 920 Madison Avenue, Memphis, Tennessee 38103 United States ; University of Tennessee Health Science Center , Department of Biomedical Engineering, 920 Madison Avenue, Memphis, Tennessee 38103 United States.

Abstract

Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.

KEYWORDS:

change detection; fuzzy logic classifier; glaucoma; nonlocal Markov field

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