doi: 10.7763/IJCTE.2013.V5.657
Procrustes Analysis for Diffusion Tensor Image Processing
Diwei Zhou1
, , ,
- 1School of Technology, University of Wolverhampton, Wolverhampton, WV1 1LY, UK.
- 2Department of Statistics, University of South Carolina, Columbia, SC 29208, USA.
- 3Department of Mathematics, Royal Holloway, University of London, Egham, TW20 0EX, UK.
- 4School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK
Abstract
There is an increasing need to develop processing tools for diffusion tensor image data with the consideration of the non-Euclidean nature of the tensor space. In this paper Procrustes analysis, a non-Euclidean shape analysis tool under similarity transformations (rotation, scaling and translation), is proposed to redefine sample statistics of diffusion tensors. A new anisotropy measure Procrustes Anisotropy (PA) is defined with the full ordinary Procrustes analysis. Comparisons are made with other anisotropy measures including Fractional Anisotropy and Geodesic Anisotropy. The partial generalized Procrustes analysis is extended to a weighted generalized Procrustes framework for averaging sample tensors with different fractions of contributions to the mean tensor. Applications of Procrustes methods to diffusion tensor interpolation and smoothing are compared with Euclidean, Log-Euclidean and Riemannian methods.
Keywords
- Non-euclidean metric
- diffusion tensor
- procrustes analysis
- anisotropic diffusion
How to Cite
Diwei Zhou, Ian L. Dryden, Alexey A. Koloydenko, and Li Bai, "Procrustes Analysis for Diffusion Tensor Image Processing," International Journal of Computer Theory and Engineering, vol. 5, no. 1, pp. 108-113, 2013. https://doi.org/10.7763/IJCTE.2013.V5.657
Copyright & License
Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).