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Supervised Segmentation of Fiber Bundl
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.