Glaucoma progression detection using nonlocal Markov random field prior

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Belghith, A; Bowd, C; Medeiros, FA; Balasubramanian, M; Weinreb, RN; Zangwill, LM

Published Date

  • October 1, 2014

Published In

Volume / Issue

  • 1 / 3

Electronic International Standard Serial Number (EISSN)

  • 2329-4310

International Standard Serial Number (ISSN)

  • 2329-4302

Digital Object Identifier (DOI)

  • 10.1117/1.JMI.1.3.034504

Citation Source

  • Scopus