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Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering
Birkan Tunç1, Alex R. Smith1, Demian Wasserman2, Xavier Pennec3, William M. Wells2, Ragini Verma1, and Kilian M. Pohl1
1Section of Biomedical Image Analysis, University of Pennsylvania, USA
2Brigham and Women’s Hospital, Harvard Medical School, USA
3INRIA - Sophia Antipolis, France
Abstract. The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fibers decoding their connectivity to gray matter regions. We then simplify the clustering task by first deriving a compact encoding of our representation via the logit transformation. Furthermore, we define a distance between fibers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting fiber bundles without needing to register the corresponding scans to a common coordinate system. We confirm these qualitative findings via a simple statistical analyse of the fiber bundles.
Keywords: Tractography, connectivity, fiber clustering, log odds
LNCS 7917, p. 730 ff.
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© Springer-Verlag Berlin Heidelberg 2013