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Probabilistic Clustering and Shape Modelling of White Matter Fibre Bundles using Regression Mixtures

By Nagulan Ratnarajah, Andy Simmons, Oleg Davydov and Ali Hojjatoleslami


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

Topics: RC0321, QA801, QA273, QA611, QM
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