Abstract
This paper presents a novel white matter fibre tractography approach using average curves of probabilistic fibre tracking measures. We compute ”representative” curves from the original probabilistic curve-set using two different averaging methods. These typical curves overcome a number of the limitations of deterministic and probabilistic approaches. They produce strong connections to every anatomically distinct fibre tract from a seed point and also convey important information about the underlying probability distribution. A new clustering algorithm is employed to separate fibres into branches before applying averaging methods. The performance of the technique is verified on a wide range of seed points using a phantom dataset and an in vivo dataset.
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Ratnarajah, N., Simmons, A., Davydov, O., Hojjat, A. (2010). A Novel White Matter Fibre Tracking Algorithm Using Probabilistic Tractography and Average Curves . In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_81
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DOI: https://doi.org/10.1007/978-3-642-15705-9_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15704-2
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