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Abstract #0115

A Novel Clustering Algorithm for Application to Large Probabilistic Tractography Data Sets

Robert Elton Smith1,2, Jacques-Donald Tournier1,2, Fernando Calamante1,2, Alan Connelly1,2

1Brain Research Institute, Florey Neuroscience Institutes (Austin), Heidelberg West, Victoria, Australia; 2Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia

Current clustering methodologies are not able to process very large data sets, such as those generated using probabilistic tractography. We propose a novel clustering algorithm designed specifically to handle a very large number of tracks, which is therefore ideally suited for processing whole-brain probabilistic tractography data. A hierarchical clustering stage identifies major white matter structures from the large number of smaller clusters generated. The method is demonstrated on a 1,000,000 track whole-brain in-vivo data set.