医学图像的配准与熔合是现代医疗中不可或缺的一部分。
Medical image registration and fusion is an indispensable part of modern medical treatment.
基于互信息的图像配准方法，已被广泛用于医学图像的配准。
Mutual information has been widely applied in medical image registration because of the merits of non-preprocessing and high automation.
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.
医学图像的配准和融合是医学图像处理的一个新的领域，其目的是为医生提供更多的诊断信息。
Medical image registration and merging is a new area in medical image processing the purpose is to provide more diagnostic information to the physicians.
基于互信息的图像配准方法具有自动化程度高、配准精度高等优点，已被广泛应用于医学图像的配准。
Image registration based on mutual information is of high automatization and high accuracy in registration. Hence, it has been widely exploited in medical image registration.
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.
基于互信息的医学图像配准算法近来获得了广泛的应用。
The medical image registration method based on mutual information has been widely used recently.
在现代医学中，DSA是X光片血管可视化技术的重要组成部分之一，在DSA中图像配准对消除运动伪影起着关键作用。
In modern medicine, DSA is a powerful technique in visualizations of blood vessels of X-ray images. In DSA, the most important way for motion correction is image registration.
本文综述了基于互信息的医学图像配准技术和应用。
This paper summarize the technology and application of mutual information based medical image registration.
方法提出了一种基于互信息和模拟退火算法的医学图像配准方法。
METHODS a multimodality medical image registration method based on mutual information and simulated annealing was presented.
给出了一种基于最大互信息和边缘互方差的医学图像配准算法。
Gives a new algorithm of medical image registration based on maximum of mutual information and edge correlative deviation.
它不仅可以将同一病人的切片图像进行配准融合以便于医学诊断，而且可以将不同病人之间以及病人与标准图谱之间进行处理。
We can't only do this in different cross-sections of same patient, but also do it between different patients, and images of patient to standard atlas.
应用此算法求解医学图像配准的优化模型，可实现时间序列脑功能图像的高精度配准。
We find out higher accurate registration of the time series brain functional images by solving the images registration optimization problem.
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.
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。
Results Using this method for medical image elastic registration, rapid and accurate registration between standard and deformed images was achieved.
基于互信息的图像配准方法具有自动化程度高、配准精度高等优点，近年来在医学图像配准中得到广泛应用。
Mutual information-based image registration has been widely exploited due to its high-level automation and accuracy, especially in medical image registration.
将所有点对坐标值代入刚体变换线性方程组，用最小二乘法求出从图像空间到手术空间的刚体变换矩阵，最终实现医学图像标志点的自动识别和配准。
Substituting coordinates of all pairs of points into rigid transform equations, one could use least squares to get the rigid transform matrix from image space to surgical space.
医学图像配准与信息融合是当代信息科学、计算机图像技术与当代医学等多学科交叉的一个研究领域。
Medical image registration and fusion is a crossing research topic of information science, computer image technology and modern medicine.
这种将不同模式的图像信息整合成一种新模式的图像称为医学图像的融合，而融合的第一步先要配准。
The new image which is integrated from different modes images called the medical image fusion and the first step of fusion is image registration.
基于互信息的配准方法，包括互信息和归一化互信息方法，是目前医学图像配准中无创、自动且精度很高的一种方法，已经被广泛应用。
Image registration methods based on mutual information, including mutual information and normalized mutual information, have been accepted as the most accurate and efficient methods.
医学图像配准作为图像融合的先决条件，它的研究是医学图像处理领域的热点。
Medical image registration as a prerequisite for image fusion, its research is a hot in the area of medical image processing.
目的探索建立三维核医学图像配准和融合进行心肌存活评价的定量分析方法。
Objective To establish a new quantitative analyzing method for myocardial viability evaluation based on the 3-d registration and fusion of the nuclear medicine images.
摘要：提出了一种基于光流场的医学图像弹性配准方法。
An elastic registration technique for medical image based on optical flow field is presented in this paper.
医学图像配准是医学图像处理研究的一个重要方向，而基于互信息量的配准方法是广泛应用的一种配准方法。
Medical image registration is a key issue of medical image processing. One popularly applied registration method is mutual-information-based image registration.
医学图像非刚性的配准在医学诊断和治疗计划中起着重要的作用，学者们为此提出了各种非刚性配准算法。
Non_rigid registration plays an important role in medical diagnosis and therapy scheme. Scholars bring forward of algorithms many kinds.
实验表明本文方法在单模态医学图像配准中能较好地确定标志点对应关系，同时该方法快速、可行的。
The proposed technique is shown to be feasible and rapid in the experiments of various mono-modal medical images.
这四个组成部分在图像配准操作中分别担任不同的角色，从而构建出一个简单、快速、稳定的医学影像配准框架。
These four parts acted as different roles in medical images registration and constructed a simple, rapid and stable medical images registration framework.
实验结果表明，该方法能够有效平滑互信息的局部极值，减少错误的医学图像配准。
The experimental results show that the method can smooth the local extremums of mutual information and reduce the possibility of wrong medical image registration.
实验结果表明，该方法能够有效平滑互信息的局部极值，减少错误的医学图像配准。
The experimental results show that the method can smooth the local extremums of mutual information and reduce the possibility of wrong medical image registration.
应用推荐