A BAYESIAN FRAMEWORK FOR GLAUCOMA PROGRESSION DETECTION USING HEIDELBERG RETINA TOMOGRAPH IMAGES
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2013, Vol 4, Issue 9
Abstract
Glaucoma, the second leading cause of blindness in the United States, is an ocular disease characterized by structural changes of the optic nerve head (ONH) and changes in visual function. Therefore, early detection is of high importance to preserve remaining visual function. In this context, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser tomograph, is widely used as a research tool as well as a clinical diagnostic tool for imaging the optic nerve head to detect glaucoma and monitor its progression. In this paper, a glaucoma progression detection technique is proposed using the HRT images. Contrary to the existing methods that do not integrate the spatial pixel dependency in the change detection map, we propose the use of the Markov Random Field (MRF) to handle a such dependency. In order to estimate the model parameters, a Monte Carlo Markov Chain procedure is used. We then compared the diagnostic performance of the proposed framework to existing methods of glaucoma progression detection.
Authors and Affiliations
Akram Belghith, Christopher Bowd, Madhusudhanan Balasubramanian, Robert Weinreb, Linda Zangwill
Keywords
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- EP ID EP109683
- DOI 10.14569/IJACSA.2013.040934
- Views 104
- Downloads 0
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