Glaucoma is an ocular disease characterized by distinctive changes in the optic 
nerve head (ONH) and visual field. Glaucoma can strike without symptoms and 
causes blindness if it remains without treatment. Therefore, early disease 
detection is important so that treatment can be initiated and blindness 
prevented. In this context, important advances in technology for non-invasive 
imaging of the eye have been made providing quantitative tools to measure 
structural changes in ONH topography, an essential element for glaucoma 
detection and monitoring. 3D spectral domain optical coherence tomography 
(SD-OCT), an optical imaging technique, has been commonly used to discriminate 
glaucomatous from healthy subjects. In this paper, we present a new framework 
for detection of glaucoma progression using 3D SD-OCT images. In contrast to 
previous works that the retinal nerve fiber layer (RNFL) thickness measurement 
provided by commercially available spectral-domain optical coherence tomograph, 
we consider the whole 3D volume for change detection. To integrate a priori 
knowledge and in particular the spatial voxel dependency in the change detection 
map, we propose the use of the Markov Random Field to handle a such dependency. 
To accommodate the presence of false positive detection, the estimated change 
detection map is then used to classify a 3D SDOCT image into the 
"non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic 
classifier. We compared the diagnostic performance of the proposed framework to 
existing methods of progression detection.