Abstract: A computer-implemented method for visualization of diffusion tensor images includes providing a diffusion tensor image input and providing a volume of interest within the diffusion tensor image input. The method includes determining a plurality of direction-based classifications of the volume of interest, wherein classes are defined by a set of parameters, An optimal solution is then selected within the classifications by using a criterion defined as a ratio of inertia indicators. To represent the chosen classification, a cone graph is determined for each of the directional classes to be displayed or stored, each cone pair being the geometrical interpretation of the class parameters. The method further includes determining a spherical scatterplot of the volume of interest augmented with a cone graph for visualization of at least one of the directional classes, and displaying and/or storing the scatterplot.

Abstract: A computer-implemented method for detection of fiber pathways includes determining initial data as a subset of voxels that have been selected from a diffusion tensor image by applying a threshold to fractional anisotropy values, determining a cluster of points with highly collinear diffusion directions, and performing region growth to find suitable seed points in a plane that is normal to the cluster's mean direction, starting from the center of the cluster. The method further includes tracing pathways from the seed points, eliminating voxels of the subset from the initial data that are close to any of the pathways using a distance threshold, displaying a visualization of a selection of voxels used as seed points and of the pathways traced from the seed points.

Abstract: A computer-implemented method for visualization of diffusion tensor images includes providing a diffusion tensor image input and providing a volume of interest within the diffusion tensor image input. The method includes determining a plurality of direction-based classifications of the volume of interest, wherein classes are defined by a set of parameters, An optimal solution is then selected within the classifications by using a criterion defined as a ratio of inertia indicators. To represent the chosen classification, a cone graph is determined for each of the directional classes to be displayed or stored, each cone pair being the geometrical interpretation of the class parameters. The method further includes determining a spherical scatterplot of the volume of interest augmented with a cone graph for visualization of at least one of the directional classes, and displaying and/or storing the scatterplot.

Abstract: A computer-implemented method for detection of fiber pathways includes determining initial data as a subset of voxels that have been selected from a diffusion tensor image by applying a threshold to fractional anisotropy values, determining a cluster of points with highly collinear diffusion directions, and performing region growth to find suitable seed points in a plane that is normal to the cluster's mean direction, starting from the center of the cluster. The method further includes tracing pathways from the seed points, eliminating voxels of the subset from the initial data that are close to any of the pathways using a distance threshold, displaying a visualization of a selection of voxels used as seed points and of the pathways traced from the seed points.