在图像配准中常常需要把来源于不同成像设备的图像进行配准处理，这些不同成像模式的图像称为多模态图像。
The images which need to register each other are always beyond one-modal images. In many cases, it is required to align images taken from different imaging apparatus.
方法提出一种基于自由变形模型的多模态医学图像的非刚性配准的方法。
Method A non-rigid registration method of multimodal medical images based on Free Form Deformation(FFD) was proposed.
本文首先研究了基于多模态刚性图像配准的图像定位系统的有关设计和实现。
Firstly, we researched the design and implementation of the image guided localization system based on multimodality image registration.
多模态医学图像的配准在医学诊断和治疗计划中起着重要的作用。
Multimodality medical image registration has important applications in clinical diagnosis and therapy planning.
为了实现多模态医学图像的配准融合，提出一种加快寻优的医学图像互信息配准算法实现CT和MR图像的配准。
A new medical image mutual information registration method, which can speedup the optimized process is proposed for CT and MR medical image auto rigid registration.
根据测试的结果，我们可以认为这一配准方法可以很好的解决多模态图像的配准问题，具有较高的精度和鲁棒性。
The results tell us this method is very suitable for multi-modal images and has high precision and robust.
为了实现多模态图像间的快速自动配准，本文提出了基于角点检测结合区域匹配的图像自动配准算法。
In order to realize fast automatic registration among multimodal images, an image registration method using both corners and area-based matching is presented in thesis.
提出了一种全自动、多模态的信息融合解决方案用于配准视频图像和磁跟踪数据。
The paper reports a fully-automated, multiple-modality sensor data registration scheme between video and magnetic tracker data.
为了准确、可靠地配准多模态医学图像，提出了一种基于互信息的全局优化配准算法。
A global optimization method based on mutual information is proposed for multimodality medical image registration.
为了准确、可靠地配准多模态医学图像，提出了一种基于互信息的全局优化配准算法。
A global optimization method based on mutual information is proposed for multimodality medical image registration.
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