Ratnarajah, N. and Simmons, A. and Davydov, O. and Hojjatoleslami, A. (2011) Probabilistic Clustering and Shape Modelling of White Matter Fibre Bundles using Regression Mixtures. In: MICCAI 2011, 18-22 September 2011, Toronto, Canada. (Unpublished)
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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 expectation-maximisation (EM) algorithm to estimate cluster membership. The result of clustering is the probabilistic assignment of fibre trajectories to each cluster and an estimate of the cluster parameters. A statistical model is calculated for each clustered fibre bundles using fitted parameters of the probabilistic clustering. We illustrate the potential of our clustering approach on synthetic data and real data.
| Item Type: | Conference or workshop item (Paper) |
|---|---|
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Q Science > QA Mathematics (inc Computing science) > QA801 Analytic mechanics Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities Q Science > QA Mathematics (inc Computing science) > QA440 Geometry > QA611 Topology Q Science > QM Human anatomy |
| Divisions: | Faculties > Science Technology and Medical Studies > School of Biosciences > Biomedical Research Group |
| Depositing User: | Sayed Ali Hojjatoleslami |
| Date Deposited: | 12 May 2011 17:10 |
| Last Modified: | 20 Mar 2012 11:09 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/27765 (The current URI for this page, for reference purposes) |
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