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IPMI
2011
Springer

Nonnegative Factorization of Diffusion Tensor Images and Its Applications

8 years 1 months ago
Nonnegative Factorization of Diffusion Tensor Images and Its Applications
This paper proposes a novel method for computing linear basis images from tensor-valued image data. As a generalization of the nonnegative matrix factorization, the proposed method aims to approximate a collection of diffusion tensor images using nonnegative linear combinations of basis tensor images. An efficient iterative optimization algorithm is proposed to solve this factorization problem. We present two applications: the DTI segmentation problem and a novel approach to discover informative and common parts in a collection of diffusion tensor images. The proposed method has been validated using both synthetic and real data, and experimental results have shown that it offers a competitive alternative to current state-of-the-arts in terms of accuracy and efficiency.
Yuchen Xie, Jeffrey Ho, Baba C. Vemuri
Added 30 Aug 2011
Updated 30 Aug 2011
Type Journal
Year 2011
Where IPMI
Authors Yuchen Xie, Jeffrey Ho, Baba C. Vemuri
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