  Diffusion Tensor Imaging (DTI) allows estimating the position, orientation
and dimension of bundles of nerve pathways. This non-invasive imaging technique
takes advantage of the diffusion of water molecules and determines the
diffusion coefficients for every voxel of the data set. The identification of
the diffusion coefficients and the derivation of information about fiber
bundles is of major interest for planning and performing neurosurgical
interventions. To minimize the risk of neural deficits during brain surgery as
tumor resection (e.g. glioma), the segmentation and integration of the results
in the operating room is of prime importance. In this contribution, a robust
and efficient graph-based approach for segmentating tubular fiber bundles in
the human brain is presented. To define a cost function, the fractional
anisotropy (FA) is used, derived from the DTI data, but this value may differ
from patient to patient. Besides manually definining seed regions describing
the structure of interest, additionally a manual definition of the cost
function by the user is necessary. To improve the approach the contribution
introduces a solution for automatically determining the cost function by using
different 3D masks for each individual data set.
