利用边缘和表面特征实现脑图像的自动配准

本文中我们使用基于CT、MR和PET图像等值特征表面的配准算法对多模医学图像进行了配准研究，在CT、MR和PET的原始图像中提取等值特征表面，进行图像的几何对准，并对结果进行初步评估，同时对该算法的稳健性，搜索最近点策略和采样策略进行了研究，结果表明；这种方法能够达到亚像素级的配准精度，是一种稳健、高精度、全自动的配准方法。     

基于图像特征的CT-MR图像配准

本文采用了基于图像特征的相关性算法对3－D CT－MR图像进行配准。首先使用基于局部坐标系的二阶导数算子对CT和MR图像进行处理,获取由图像的“脊”或“谷”,组成的特征图像然,然后在进行刚体变换条件下对特征图像进行相关计算,使其相关系数达到最大,从而获取配准后的图像。该方法不需在成像过程中人为增加标记物,后续处理中不需人为干预,是一种自动的图像配准方法,最后本文给出了这一方法用于实际数据的结果。     

一种基于距离图和分级配准策略的人脑图像非刚体配准方法

目的:基于特征的配准方法中,特征配准结果的好坏决定着最终配准结果的好坏.方法:本文提出了一种基于距离图和分级配准策略的人脑MR图像非刚体配准方法.首先使用C-V模型完成特征的提取工作,并设计了相应的预处理过程,来获得适合进行特征配准的边界点集.接着采用分级配准策略来完成特征配准工作,包括逐条仿射配准和非刚体配准两个子步骤.为了方便计算,距离图被引入到算法过程中,以方便点距的查询.最终的全局配准工作基于特征配准的结果进行,配准问题转化为相应的插值问题进行求解.结果:针对两组初始差异较大的脑部图像进行实验,均可以达到配准要求.结论:实验结果证明,新算法能够适合初始偏差较大配准场合的需求.     

鲍威尔和模拟退火优化算法结合的多分辨率三维图像配准

基于互信息的全局配准算法是近年图像配准研究的热点之一，此法具有精度高、鲁棒性强的特点。现将互信息作为相似性测度，采用模拟退火与鲍威尔相结合的多分辨率搜索优化策略，对临床使用的三维CT、MR图像进行了刚体配准。实验结果表明此方法能有效地防止优化陷入局部极值，具有良好的实用性。     

一种基于小波变换的配准特征点自动标记算法

以颅脑CT图像为研究对象,提出了一种基于小波变换的自动标记非刚性配准所需对应特征点的算法.这种算法充分考虑了颅脑CT图像的像素点及其临域的纹理特征,通过进行小波变换建立对应于每个像素点的多分辨率小波特征向量,并以小波特征向量间的差异作为判别依据,在目标图像中标记非刚性配准所需的对应特征点.一系列的实验结果表明,这种基于小波变换的算法能够准确地在目标图像中标记出配准所需的对应特征点,可以作为基于特征的非刚性配准对应特征点自动标记的参量之一.     

多模态图像配准在HIFU定位系统中的应用

在交互式图像导航HIFU(高强度聚焦超声)治疗系统中,需要对病灶目标进行非常精确的实时成像和定位.而现有的超声成像技术很难单独完成这个任务.本文提出了一种用手术前MRI三维图像与手术中的超声图像进行配准的方法,对手术前MR图像和手术中超声图像两种模态下都可见的血管进行配准.配准算法采用遗传算法和共轭梯度法结合的优化策略来最小化目标函数,并设计了两个实验对配准方法进行评价,从实验结果看这种方法从配准精度和收敛速度上都要优于另外的两种经典算法.     

利用相关比相似性测度多分辨率配准MR和CT医学图像的方法

本文提出了有效的、能被临床应用所接受的磁共振（MR）和CT医学图像配准方法。在基于体素灰度的医学图像配准领域。本文采用了全新的相关比相似性测度作为配准的测度准则。具体设计时，采用了加速的多分辨率配准方案，对方案中涉及的几何变换选取、重采样、多分辨率体数据表达及最优化方法进行了设计分析。最后，利用本文提出的多分辨率配准方法，对MR和CT临床医学图像进行配准，给出了令人满意的效果。     

医学图像配准技术及其研究进展

目的:对近年来的医学图像配准技术及其研究进展情况进行详尽地综述和讨论,从而为开展医学图像配准技术在医学图像三维重建、医学图像可视化和定量分析方面的研究提供参考.方法:首先,查阅国内外近年来医学图像配准技术研究的权威文献;然后,深入分析和研究这些文献所介绍方法的特点、存在的问题,并针对存在的问题提出可能的解决方案.结果:通过对近年来医学图像配准算法的最新研究进展情况进行深人细致地分析和讨论,在比较了一些典型算法的特点及其应用的基础上,对医学图像配准技术的发展进行了展望.结论:使用最优化策略改进图像配准质量以及对非刚体图像配准的研究是今后医学图像配准的发展方向.     

一种基于感兴趣点旋转不变性特征的图像配准新方法

目的:图像配准是图像处理领域重要的研究方向,是图像融合、图像重建和图像分析等研究的基础。在图像配准的主要方法中,基于图像特征的配准方法和基于图像灰度的配准方法各有优缺点,通过结合这两种方法的优点,我们提出了一种基于感兴趣点的旋转不变性特征图像配准的新方法。方法:首先利用Harris角点检测技术,提取模板图像和目标图像的感兴趣点。然后把感兴趣点的旋转不变形特征和灰度值组成图像的特征描述向量,并提出新的代价函数。最后采用分级优化的策略优化代价函数,在配准初期,采用显著的特征点进行配准,以保证配准的速度与鲁棒性,随后通过逐步增加特征点的数量,则保证了配准的精度。结果:为显示本文方法的优越性,实验利用本文方法和基于互信息的B样条方法分别对标准测试图像进行配准,实验结果表明,本文方法较基于互信息的B样条方法在配准精度上有明显提高。结论:本文方法在保持配准鲁棒性的前提下,获得了较高的配准精度。     

多模态医学图像配准技术研究进展

多模态医学图像配准技术是目前医学图像处理中的研究热点,对于临床诊断和治疗有重要意义.首先分析了图像配准的过程,然后根据待配准图像的特征,包括图像的外部特征和内部特征,系统且详细地介绍了多模态医学图像配准的常用方法,并对这些方法进行了比较.     

基于特征提取的脑部医学图像配准研究

目的 基于特征的配准算法具有鲁棒性强、针对性好等显著优势,在图像配准领域被广泛应用,但是该类方法的精度受图像间特征构建和环境噪声影响大,该研究旨在对其缺点进行改进。方法 该研究基于SURF和ORB两种算法,提出了SURF-ORB算法,将参考图像与待配准图像分成上下两部分分别配准。在配准过程中,首先对SURF提取的图像特征点的Harris响应值进行优化,并使用灰度质心法确定特征点主方向。然后计算rBRIEF（旋转BRIEF）描述子,并使用汉明距离进行特征点匹配。最后加入RANSAC精匹配算法,剔除误匹配点。结果和结论 该研究通过对比分析SURF、ORB、SURF-ORB这3种算法的配准结果、抗噪声能力及多模态配准能力,验证了SURF-ORB算法具有较高的配准精度、配准速度和抗噪声能力。文章的创新之处该研究首次将SURF和ORB两种算法进行结合并应用于脑部横断面图像。     

The effect of automated landmark identification on morphometric analyses

Morphometric analysis of anatomical landmarks allows researchers to identify specific morphological differences between natural populations or experimental groups, but manually identifying landmarks is time‐consuming. We compare manually and automatically generated adult mouse skull landmarks and subsequent morphometric analyses to elucidate how switching from manual to automated landmarking will impact morphometric analysis results for large mouse (Mus musculus) samples (n = 1205) that represent a wide range of ‘normal’ phenotypic variation (62 genotypes). Other studies have suggested that the use of automated landmarking methods is feasible, but this study is the first to compare the utility of current automated approaches to manual landmarking for a large dataset that allows the quantification of intra‐ and inter‐strain variation. With this unique sample, we investigated how switching to a non‐linear image registration‐based automated landmarking method impacts estimated differences in genotype mean shape and shape variance‐covariance structure. In addition, we tested whether an initial registration of specimen images to genotype‐specific averages improves automatic landmark identification accuracy. Our results indicated that automated landmark placement was significantly different than manual landmark placement but that estimated skull shape covariation was correlated across methods. The addition of a preliminary genotype‐specific registration step as part of a two‐level procedure did not substantially improve on the accuracy of one‐level automatic landmark placement. The landmarks with the lowest automatic landmark accuracy are found in locations with poor image registration alignment. The most serious outliers within morphometric analysis of automated landmarks displayed instances of stochastic image registration error that are likely representative of errors common when applying image registration methods to micro‐computed tomography datasets that were initially collected with manual landmarking in mind. Additional efforts during specimen preparation and image acquisition can help reduce the number of registration errors and improve registration results. A reduction in skull shape variance estimates were noted for automated landmarking methods compared with manual landmarking. This partially reflects an underestimation of more extreme genotype shapes and loss of biological signal, but largely represents the fact that automated methods do not suffer from intra‐observer landmarking error. For appropriate samples and research questions, our image registration‐based automated landmarking method can eliminate the time required for manual landmarking and have a similar power to identify shape differences between inbred mouse genotypes.     

A novel magnetic resonance-positive emission image registration based on morphology

Structures were obtained from images with morphological characteristics to register with voxel-based method. We applied simple morphological operations to obtain human brain cortex and chose normalized mutual information as similarity measure for the geometric alignment of PET and MR images. Evaluation used nine patients, and the results showed that sub-voxel accuracy was achieved and the registration process was significantly more rapid. Thus this new automated multi-modality registration method is more robust and has high accuracy.     

灰度级别对基于互信息医学图像配准方法的影响

医学图像配准在医学图像处理领域中已经被广泛使用。基于互信息配准的方法具有自动化程度高、配准精度高等优点。基于互信息的配准方法实质上是一种进行灰度统计和计算的方法 ,因此同一图像采用不同的灰度表示必然会影响配准结果。在分析灰度级别的压缩对于图像质量的影响和基于互信息配准方法的影响的基础上 ,进行了一系列的多模态医学图像配准试验 ,从配准精度和计算时间两个方面比较了不同的灰度级别对图像配准的影响。在详细分析和比较不同级别图像配准结果的基础上 ,给出了基于互信息配准时所采用的合理灰度级别的建议。     

Non-linear registration for brain images by maximising feature and intensity similarities with a Bayesian framework

The objective of this work was to provide a new, precise registration of the cortical mantle with a non-linear transformation. Image registration is broadly classified into two types, using intensity similarity and feature similarity. Whereas the former approach has merit in global brain matching, the latter provides a fast registration centred on a region of interest. The hybrid registration proposed in this paper was achieved using a Bayesian framework, which consisted of a likelihood model including intensity similarity and a prior model including feature information and a smoothing constraint. In this approach, each voxel was spatially transformed, so that the distance between corresponding features was shortened and also so that the intensity correlation was maximised. The result of the hybrid method clearly showed a good match of global brain (r=0.930) by including intensity similarity. Moreover, this method compensated for the approximated sulcus of the feature-based method with intensity information, so that the geometric shape and thickness of the sulcus at the feature-defined region was likely to be registered. The accuracy in the feature-defined area was improved by 33.4% and 7.5% compared with feature-based and intensity-based methods, respectively.     

基于互信息的多分辨率三维脑图像配准方法

在3D多模医学图像的配准方法中，最大互信息法精度高，鲁棒性强，使用范围广，本文将归一化互信息作为相似性测度，采用不同的采样范围和采样子集，使用Powell多参数优化法和Brent一维搜索算法对3DCT，MR和PET脑图像进行了刚体配准，为了加快配准速度，使用了多分辨的金字塔方法，对PET图像采用基于坐标的阈值选取方法对图像进行分割预算法，消除了大部分放射状背景伪影，美国万德贝尔大学对结果进行的评估证明配准精度可达亚体元级。     

Automated, Foot-Bone Registration Using Subdivision-Embedded Atlases for Spatial Mapping of Bone Mineral Density

We present an atlas-based registration method for bones segmented from quantitative computed tomography (QCT) scans, with the goal of mapping their interior bone mineral densities (BMDs) volumetrically. We introduce a new type of deformable atlas, called subdivision-embedded atlas, which consists of a control grid represented as a tetrahedral subdivision mesh and a template bone surface embedded within the grid. Compared to a typical lattice-based deformation grid, the subdivision control grid possesses a relatively small degree of freedom tailored to the shape of the bone, which allows efficient fitting onto subjects. Compared with previous subdivision atlases, the novelty of our atlas lies in the addition of the embedded template surface, which further increases the accuracy of the fitting. Using this new atlas representation, we developed an efficient and fully automated pipeline for registering atlases of 12 tarsal and metatarsal bones to a segmented QCT scan of a human foot. Our evaluation shows that the mapping of BMD enabled by the registration is consistent for bones in repeated scans, and the regional BMD automatically computed from the mapping is not significantly different from expert annotations. The results suggest that our improved subdivision-based registration method is a reliable, efficient way to replace manual labor for measuring regional BMD in foot bones in QCT scans.     

基于互信息的人脑图像配准研究

近来利用互信息进行多模医学图像配准已成为医学图像处理领域的热点,人脑多模医学图像配准对研究神经组织的结构功能关系和引导神经外科手术有着重要的指导意义,本文描述了一种基于互信息的人脑图像配准方法,我们将这种方法应用于图像的几何对准并给出了初步的评估结果,同时,我们还就归一化互信息、多分辨率策略,多种插值和优化算法对配准速度和精度的影响作了讨论,由于不需要对不同成像模式下的图像灰度间的关系作任何假设,互信息法是一种稳健性强、可广泛应用于基于体素的多模医学图像的配准方法。