@article {1433794,
	title = {A kernel-based approach for user-guided fiber bundling using diffusion tensor data},
	journal = {Conf Proc IEEE Eng Med Biol Soc},
	volume = {1},
	year = {2006},
	month = {2006},
	pages = {2626-9},
	abstract = {This paper describes a novel user-guided method for grouping fibers from diffusion tensor MRI tractography into bundles. The method finds fibers, that passing through user-defined ROIs, still fit to the underlying data model given by the diffusion tensor. This is achieved by filtering the data and the ROIs with a kernel derived from a geodesic metric between tensors. A standard approach using binary decisions defining tracts passing through ROIs is critically dependent on ROIs that includes all trace lines of interest. The method described in this paper uses a softer decision mechanism through a kernel which enables grouping of bundles driven less exact, or even single point, ROIs. The method analyzes the responses obtained from the convolution with a kernel function along the fiber with the ROI data. Results in real data shows the feasibility of the approach to fiber bundling.},
	keywords = {Algorithms, Artificial Intelligence, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Nerve Fibers, Neural Pathways, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1557-170X},
	doi = {10.1109/IEMBS.2006.259829},
	author = {San Jos{\'e} Est{\'e}par, Ra{\'u}l and Kubicki, Marek and Shenton, Martha and Westin, Carl-Fredrik}
}