High level group analysis of FMRI data based on dirichlet process mixture models
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Knowledge-Based classification of neuronal fibers in entire brain
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Hierarchical fiber clustering based on multi-scale neuroanatomical features
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
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In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learnt from data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learnt from training data without supervision, they can be used as priors to cluster/classify fibers of new subjects. When clustering fibers of new subjects, new clusters can be created for structures not observed in the training data. Our approach does not require computing pairwise distances between fibers and can cluster a huge set of fibers across multiple subjects without subsampling. We present results on multiple data sets, the largest of which has more than 120,000 fibers.