Computer-Aided Diagnosis

Glaucoma progression detection using nonlocal Markov random field prior

[+] Author Affiliations
Akram Belghith

University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, California 92093-0946, United States

Christopher Bowd

University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, California 92093-0946, United States

Felipe A. Medeiros

University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, California 92093-0946, United States

Madhusudhanan Balasubramanian

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

Robert N. Weinreb

University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, California 92093-0946, United States

Linda M. Zangwill

University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, California 92093-0946, United States

J. Med. Imag. 1(3), 034504 (Dec 29, 2014). doi:10.1117/1.JMI.1.3.034504
History: Received August 4, 2014; Accepted November 20, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Akram Belghith ; Christopher Bowd ; Felipe A. Medeiros ; Madhusudhanan Balasubramanian ; Robert N. Weinreb, et al.
"Glaucoma progression detection using nonlocal Markov random field prior", J. Med. Imag. 1(3), 034504 (Dec 29, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.034504


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