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.

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.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.