Conventional analysis of fMRI responses in neuroimaging experiments is typically voxel-wise, i.e. independent of spatial neighbourhood information. However, valid responses are likely to be spatially clustered and connected in 3D space. Identifying spatial relations is commonly considered a pre-processing step, isotropic Gaussian filtering for noise reduction for example. Current post-processing methods consider spatial information but not temporal information; once an activation map is obtained, voxels that do not have a sufficient number of spatial neighbors are simply removed. This paper describes how we have successfully incorporated fuzzy region growing into EvIdent®, an fMRI data analysis application. The method uses spatial-temporal information to enhance spatially connected temporally related activation regions.
KEYWORDS: Image registration, Java, Data modeling, Magnetic resonance imaging, C++, Image filtering, Linear filtering, Data analysis, 3D image processing, Magnetism
EvIdent (EVent IDENTification) is an exploratory data analysis system for the detection and investigation of novelty, identified for a region of interest and its characteristics, within a set of images. For functional magnetic resonance imaging, for instance, a characteristic of the region of interest is a time course, which represents the intensity value of voxels over several discrete instances in time. An essential preprocessing step is the rapid registration of these images prior to analysis. Two dimensional image registration coefficients are obtained within EvIdent by solving a regression problem based on integration of a linearized matching equation over a set of patches in the image space. The registration method is robust to noise, offers a flexible hierarchical procedure, is easily generalizable to 3D registration, and is well suited to parallel processing. EvIdent, written in Java and C++, offers a sophisticated data model, an extensible algorithm framework, and a suite of graphical user interface constructs. We describe the registration algorithm and its implementation within the EvIdent software.
EvIdentTM (EVent IDENTification) is a user-friendly, algorithm-rich, graphical environment for detecting, investigating, and visualizing novelty in a set of images. Novelty is identified for a region of interest and its associated characteristics. For functional magnetic resonance imaging, for instance, a characteristic of the region of interest is a time course, which represents the intensity value of voxels over several discrete instances in time. Originally developed for a platform-specific environment using proprietary technology, a new incarnation of EvIdent has been designed using an application programming interface called VIStATM (VISualization Through Analysis). VIStA is written in JavaTM and offers a sophisticated generalized data model, an extensible algorithm framework, and a suite of graphical user interface constructs. This paper describes EvIdent and some of its features, the rationale behind the design of VIStA, and the motivations and challenges of scientific programming using Java.
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