Abstract
We present a novel approach for probabilistic clustering of white matter fibre pathways using curve-based regression mixture modelling techniques in 3D curve space. The clustering algorithm is based on a principled method for probabilistic modelling of a set of fibre trajectories as individual sequences of points generated from a finite mixture model consisting of multivariate polynomial regression model components. Unsupervised learning is carried out using maximum likelihood principles. Specifically, conditional mixture is used together with an EM algorithm to estimate cluster membership. The result of clustering is a probabilistic assignment of fibre trajectories to each cluster and an estimate of cluster parameters. A statistical shape model is calculated for each clustered fibre bundle using fitted parameters of the probabilistic clustering. We illustrate the potential of our clustering approach on synthetic and real data.
Chapter PDF
Similar content being viewed by others
References
Zhang, S., Correia, S., Laidlaw, D.H.: Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber-clustering method. IEEE Trans Vis. Comput. Graph. 14(5), 1044–1053 (2008)
O’Donnell, L.J., Westin, C.-F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Tr. Med. Im. 26(11), 1562–1575 (2007)
Li, H., Xue, Z., Guo, L., Liu, T., Hunter, J., Wong, S.T.: A hybrid approach to automatic clustering of white matter fibers. Neuroimage 49(2), 1249–1258 (2010)
Maddah, M., Grimson, W.L., Warfield, S.K.: A unified framework for clustering and quantitative analysis of whitematter fiber tracts. Med. Im. An. 12(2), 191–202 (2008)
Witelson, S.F.: Hand and sex differences in the isthmus and genu of the human corpus callosum. Brain 112, 799–835 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ratnarajah, N., Simmons, A., Hojjatoleslami, A. (2011). Probabilistic Clustering and Shape Modelling of White Matter Fibre Bundles Using Regression Mixtures. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-23629-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23628-0
Online ISBN: 978-3-642-23629-7
eBook Packages: Computer ScienceComputer Science (R0)