title: Nonnegative factorization of diffusion tensor images and its applications.
abstract: 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.
authors.affiliation: Department of CISE, University of Florida, Gainesville, FL 32611, USA. yxie@cise.ufl.edu
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Xie
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Ho
authors.forename: Baba C
authors.initials: BC
authors.lastname: Vemuri
mesh_terms: D000465:Algorithms; D000818:Animals; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity