Elastic registration based on thin plate spline interpolation is deeply studied.
薄板样条插值是应用较多的弹性配准方法。
An elastic registration technique for medical image based on optical flow field is presented in this paper.
摘要：提出了一种基于光流场的医学图像弹性配准方法。
Two elastic registration methods based on two sets of corresponding feature points for human eyeground and brain images are presented.
使用薄板样条方法，利用两个对应标志点集对眼底、脑等多种医学图像进行弹性配准。
Results Using this method for medical image elastic registration, rapid and accurate registration between standard and deformed images was achieved.
结果运用此方法进行医学图像的弹性配准，实现了标准图像与变形图像的快速、准确配准。
Finally, the machine learning based frame is combined with the existing registration arithmetic to complete the elastic registration of stereo NMR brain images.
最后将基于机器学习的框架和现有的配准算法相结合，完成立体核磁共振脑图像弹性配准。
The algorithm was constructed by integrating the elastic registration algorithm based on B-spline and the apriori knowledge of the deformation field into a MRF model.
本研究提出了一种新的基于先验知识的弹性配准算法，首次把马尔可夫模型应用于图像的弹性配准方面。
There are two categories of methods for image registration: rigid and elastic.
图像的配准方法有多种，它们可以分为刚性和弹性配准两大类。
METHODS: a new deformable registration technique based on elastic model was applied for describing partial deformation.
方法：使用基于弹性体模型的形变配准方法描述图像的局部形变。
METHODS: a new deformable registration technique based on elastic model was applied for describing partial deformation.
方法：使用基于弹性体模型的形变配准方法描述图像的局部形变。
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