Xie Yuchen, Ho Jeffrey, Vemuri Baba C
Department of CISE, University of Florida, Gainesville, FL 32611, USA.
Inf Process Med Imaging. 2011;22:550-61. doi: 10.1007/978-3-642-22092-0_45.
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.
本文提出了一种从张量值图像数据中计算线性基图像的新方法。作为非负矩阵分解的推广，该方法旨在使用基张量图像的非负线性组合来逼近扩散张量图像的集合。提出了一种高效的迭代优化算法来解决此分解问题。我们展示了两个应用：DTI分割问题以及一种在扩散张量图像集合中发现信息丰富且共同部分的新方法。所提出的方法已通过合成数据和真实数据进行了验证，实验结果表明，在准确性和效率方面，它为当前的先进技术提供了具有竞争力的替代方案。
