Name
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. |
Year | Venue | Keywords |
|---|---|---|
2011 | IPMI | basis tensor image,novel method,nonnegative factorization,factorization problem,nonnegative matrix factorization,nonnegative linear combination,diffusion tensor image,novel approach,dti segmentation problem,linear basis image,algorithms |
Field | DocType | Volume |
Linear combination,Computer vision,Diffusion MRI,Pattern recognition,Tensor,Segmentation,Computer science,Artificial intelligence,Non-negative matrix factorization,Optimization algorithm,Factorization | Conference | 22 |
ISSN | Citations | PageRank |
1011-2499 | 6 | 0.45 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchen Xie | 1 | 57 | 3.08 |
| Jeffrey Ho | 2 | 2190 | 101.78 |
| B.C. Vemuri | 3 | 4208 | 536.42 |