Shape Modeling and Clustering of White Matter Fiber Tractsi
Using Fourier Descriptors

Xuwei Liang, Qi Zhuang, Ning Cao, Jun Zhang
Laboratory for Computational Medical Imaging & Data Analysis
Department of Computer Science
University of Kentucky
773 Anderson Tower
Lexington, KY 40506-0046, USA

Abstract

Reliable shape modeling and clustering of white matter fiber tracts is essential for clinical and anatomical studies that use diffusion tensor imaging (DTI) tractography techniques. In this work we present a novel scheme to model the shape of white matter fiber tracts reconstructed from DTI and cluster them into bundles using Fourier descriptors. We characterize a tract's shape by using Fourier descriptors which are effective in capturing shape properties of fiber tracts. Fourier descriptors derived from different shape signatures are analyzed. Clustering is then performed on these multi-dimensional features in conjunction with mass centers using a k-means like threshold based approach. The advantage of this method lies in the fact that Fourier descriptors achieve spatial independent representation and normalization of white matter fiber tracts which makes it useful for tract comparison across subjects. It also eliminates the need to find matching correspondences between two randomly organized tracts from whole brain tracking. Several issues related to tract shape representation and normalization are also discussed. Real DTI datasets are used to test this technique. Experiment results show that this technique can effectively separate multiple fascicles into plausible bundles.


Key words: Diffusion tensor imaging, tractography, clustering, white matter fiber, Fourier descriptors

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Download the PDF file liang4.pdf.
Technical Report CMIDA-HiPSCCS 011-08, Department of Computer Science, University of Kentucky, Lexington, KY, 2008.


The authors acknowledge the support of funding agencies and the collaborators. The research work of J. Zhang was supported in part by the US National Science Foundation under grant CCF-0527967 and CCF-0727600, in part by the National Institutes of Health under grant 1R01HL086644-01, in part by the Kentucky Science and Engineering Foundation under grant KSEF-148-502-06-186, and in part by the Alzheimer's Association under grant NIRG-06-25460.