  We present an optimized algorithm that performs automatic classification of
white matter fibers based on a multi-subject bundle atlas. We implemented a
parallel algorithm that improves upon its previous version in both execution
time and memory usage. Our new version uses the local memory of each processor,
which leads to a reduction in execution time. Hence, it allows the analysis of
bigger subject and/or atlas datasets. As a result, the segmentation of a
subject of 4,145,000 fibers is reduced from about 14 minutes in the previous
version to about 6 minutes, yielding an acceleration of 2.34. In addition, the
new algorithm reduces the memory consumption of the previous version by a
factor of 0.79.
