Supervised Segmentation of Fiber Bundles

White matter fiber tracts describe the organization and connectivity
of the human brain by means of in vivo diffusion Magnetic Resonance
Imaging (dMRI) techniques. Neurological studies are often interested
in identifying anatomically meaningful white matter fiber bundles. For
this reason the algorithms for clustering fibers into bundles have
received wide attention over the last years and a constant effort has
been sustained to incorporate prior knowledge. Despite this interest
the use of atlas-information and expert-made segmentations have been
limited. In this work in progress we focus on this kind of information
and propose an algorithm to segment a given fiber bundle of interest
from deterministic tractography data by means of binary classification
of fiber tracts. The classifier is built from expert-made examples and
addresses the case of multiple subjects. In this analysis we compare
the popular k-Nearest Neighbour classification algorithm against the
proposed dissimilarity-based approach and discuss the latter in the
context of kernel methods. We show that the proposed method provides
the means to address the supervised fiber bundle segmentation problem
from the vast majority of the algorithms of the machine learning
literature motivating new interesting lines of research.