Unsupervised white matter fiber tracts clustering methodology with application on brain MRI data

Boubchir, Larbi and Rousseau, Francois (2014) Unsupervised white matter fiber tracts clustering methodology with application on brain MRI data. In: 2014 IEEE Conference on Image Processing (ICIP), 27-30 October 2014, Paris.

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Abstract

Understanding the geometrical organization of the white matter fibers is one of the current challenges in neuroimaging. White matter fiber clustering technique appears to a corner stone to solve this problem. In this paper, we propose a rapid and efficient unsupervised white matter fiber tracts clustering methodology based on a novel fiber tract similarity metric and an approximation of the k-means algorithm. In this approach, we first define a distance metric capable to quantify the intrinsic geometry of the fiber tracts. This metric is based on a combination of the symmetric Chamfer distance and mean local orientation measures between fiber tracts. Second, we perform the randomized feature selection algorithm proposed for the k-means problem to reduce the dimensionality of the distance data matrix generated from all the fiber tracts using the defined metric. The k-means algorithm is then performed on the reduced distance matrix to cluster the fiber tracts. Finally, we evaluate the method on the synthetic data and in vivo adult brain dataset.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Chamfer distance, DTI, dMRI, distance metric, fiber clustering, k-means approximation
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	06 Jul 2015 12:34
Last Modified:	13 Oct 2019 00:37
URI:	http://nrl.northumbria.ac.uk/id/eprint/23257

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