一种用于磁共振脑图像的新型统计约束可变形配准框架。
A new statistically-constrained deformable registration framework for MR brain images.
作者信息
Xue Zhong, Shen Dinggang
机构信息
The Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, Texas, USA,
出版信息
Int J Med Eng Inform. 2009 Jan 1;1(3):357-367. doi: 10.1504/IJMEI.2009.022646.
Statistical models of deformations (SMD) capture the variability of deformations from the template image onto a group of sample images and can be used to constrain the traditional deformable registration algorithms to improve their robustness and accuracy. This paper employs a wavelet-PCA-based SMD to constrain the traditional deformable registration based on the Bayesian framework. The template image is adaptively warped by an intermediate deformation field generated based on the SMD during the registration procedure, and the traditional deformable registration is performed to register the intermediate template image with the input subject image. Since the intermediate template image is much more similar to the subject image, and the deformation is relatively small and local, it is less likely to be stuck into undesired local minimum using the same deformable registration in this framework. Experiments show that the proposed statistically-constrained deformable registration framework is more robust and accurate than the conventional registration.
变形统计模型（SMD）捕捉从模板图像到一组样本图像的变形变异性，并可用于约束传统的可变形配准算法，以提高其鲁棒性和准确性。本文采用基于小波主成分分析的SMD来约束基于贝叶斯框架的传统可变形配准。在配准过程中，模板图像通过基于SMD生成的中间变形场进行自适应扭曲，然后执行传统的可变形配准，将中间模板图像与输入的目标图像进行配准。由于中间模板图像与目标图像更为相似，且变形相对较小且局部，因此在该框架中使用相同的可变形配准陷入不期望的局部最小值的可能性较小。实验表明，所提出的统计约束可变形配准框架比传统配准更鲁棒、更准确。
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