Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Fast and resolution independent line integral convolution
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
On the Convergence of the Lagged Diffusivity Fixed Point Method in Total Variation Image Restoration
SIAM Journal on Numerical Analysis
Image Processing for Diffusion Tensor Magnetic Resonance Imaging
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A Review of Nonlinear Diffusion Filtering
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Images as embedding maps and minimal surfaces: movies, color, and volumetric medical images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Anisotropic diffusion of multivalued images with applications to color filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Color TV: total variation methods for restoration of vector-valued images
IEEE Transactions on Image Processing
Stochastic DT-MRI Connectivity Mapping on the GPU
IEEE Transactions on Visualization and Computer Graphics
| Hi-index | 0.02 |
Diffusion tensor imaging (DTI) can provide the fundamental information required for viewing structural connectivity. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. In this paper, we present a novel algorithm for extracting and visualizing the fiber tracts in the CNS specifically, the spinal cord. The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase. In the former, smoothing is achieved via a weighted TV-norm minimization which strives to smooth while retaining all relevant detail. For the fiber tract mapping, a smooth 3D vector field indicating the dominant anisotropic direction at each spatial location is computed from the smoothed data. Visualization of the fiber tracts is achieved by adapting a known Computer Graphics technique called the line integral convolution, which has the advantage of being able to cope with singularities in the vector field and is a resolution independent way of visualizing the 3D vector field corresponding to the dominant eigen vectors of the diffusion tensor field. Examples are presented to depict the performance of the visualization scheme on three DT-MR data sets, one from a normal and another from an injured rat spinal cord and a third from a rat brain.