Shape modeling and clustering of white matter fiber tracts using fourier descriptors

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 multidimensional 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 random...

• Type: Journal
• Year: 2009
• Where: CIBCB
• Authors: Xuwei Liang, Qi Zhuang, Ning Cao, Jun Zhang
• Keywords: CIBCB 2009, Computational Biology, Fiber Tracts, Fourier Descriptors, White Matter Fiber