2D/3D图像配准中的相似性测度和优化算法

在手术引导治疗中,2D/3D图像配准能辅助医生准确定位病人病灶,而准确的配准涉及相似性测度和优化算法等众多方面。为了研究相似性测度和优化算法对2D/3D图像刚性配准的影响,本文结合6种相似性测度和4种优化方法在配准"金标准"数据上进行了2D/3D图像配准实验,并从配准成功率、平均迭代次数和平均配准时间三个方面对配准结果进行了对比研究。实验结果表明,以模式强度为相似性测度,用Powell方法进行优化搜索是最佳配准组合。并且,在不改变相似性测度条件下,Powell方法是所用优化方法中配准效果最好的优化方法。     

基于改进配准测度的红外与可见光图像配准

针对在可见光与红外配准中采用传统测度算法时存在配准精度低的问题,提出了改进配准测度函数(RMF),实现可见光和红外图像高精度自适应配准.首先选用harris算子提取两幅图像的特征点;再利用改进的配准测度函数(RMF)有效的获取特征点对;最后利用RMF最大的两对匹配点计算配准参数,并进行图像配准.实验结果证明了该方法的实...     

新的Jensen-Schur测度在医学图像配准中的应用

针对传统的互信息和归一化互信息运算时间长、收敛性能和抗噪声能力差的缺点,提出了一种新的医学图像配准测度,Jensen-Schur测度.从理论上分析了该测度可用于图像配准的性质,并构造了三类Jensen-Schur测度:Jensen-Schur-alpha、Jensen-Rényi-alpha、JenserrSchur-w测度.通过对人体脑部MR/CI"和MR/PET图像的刚体配准实验,从运算时间、收敛性能、抗噪鲁棒性方面,对这三类测度、互信息和归一化互信息进行了比较和分析.实验结果表明,Jensen-Schur-alpha(alpha=2或3)和Jensen-Schur-w测度的收敛性能优于其它测度,运算速度快于其它测度,对噪声有很强的鲁棒性.最后用Jensen-Sehur-alpha(alpha=2)测度进行多模态医学图像的配准实验,结果表明效果良好.     

一种图像快速配准算法的研究

在基于小波分解和互信息测度的图像配准方法的基础上,提出一种改进的快速图像配准算法。首先,对图像进行小波分解,以分解后的图像的近似分量进行配准,利用互信息最大化作为相似性测度,并结合粒子群优化算法和鲍威尔算法为优化策略搜索最优配准参数。实验结果显示,此方法在得到较高的配准精度和鲁棒性的情况下,还大大减少了运算量,提高了配准的速度。     

融合SIFT的B样条红外图像非刚性配准

手持式广角镜头红外热像仪所拍摄的不同时刻红外图像具有刚性形变和非刚性形变,传统图像配准算法很难同时矫正刚性形变与非刚性形变,针对该问题,提出一种融合SIFT的B样条配准算法。首先在待配准图像中建立控制网格,其次运用SIFT算法寻找待配准与基准图像间的匹配点对,剔除错误匹配点对并计算出待配准图像与基准图像间的刚性变换参数,接着对控制点进行刚性变换,最后以局部强度和为测度函数,运用B样条非刚性配准算法对广角镜头引起图像的非线性进行矫正。对比实验结果表明,本文算法具有很高配准精度,能够满足实际工程精度要求。     

基于改进Hausdorff测度的医学图像配准算法

 本文针对在医学图像配准中采用Hausdorff(HD)测度算法时存在精度和计算量之间的矛盾,提出了HD测度的改进算法,在HD测度算法中首次引入非线性的Bayesian一致性函数,以达到提高配准精度和抗噪声性能的目的.实验表明,该方法有效地解决了采用传统HD距离测度算法时,因出格点和噪声点引起的误配问题,获得了较好的配准效果.     

基于对齐度和互信息的红外与可见光图像配准

针对红外与可见光图像的特点,在此提出了一种结合边缘对齐度与互信息的图像配准方法。首先通过小波变换边缘检测得到红外与可见光图像的边缘图像,并将对齐度和归一化互信息自适应加权平均得到新的相似性测度函数,最终通过计算相似性测度函数的极值求得待配准图像间的变换参数。实验结果表明,该方法可减少配准所需的时间,具有更高的精确性和鲁棒性。     

红外与可见光图像配准算法

提出把二阶互信息与对齐度相结合作为相似度函数,通过Hough变换提取两幅图像中一致性较高的直线,计算它们的对齐度,最后针对不同的图像特点,调整对齐度在新相似度函数中所占比例进行配准.实验结果表明.新算法与其他几种测度函数相比有更高的精确性和鲁棒性.     

遗传算法和Poweli法结合的多分辨率三维图像配准

基于互信息的配准方法具有精度高、鲁棒性强的特点，成为近年图像配准研究的热点，但基于互信息的目标函数存在许多局部极值，为配准的优化过程带来了很大的困难。本文以互信息作为相似性测度，采用基于小波变换的多分辨率策略，将遗传算法和Powell法结合起来对三维CT、MR图像进行了配准。实验结果表明，这种方法能够有效地克服信息函数的局部极值，配准精度达到亚像素级。     

最大化算术-几何均值距离的多传感器遥感图像配准

互信息是多模态医学图像配准的一种重要方法。它测量的是两个概率分布之间的Kullback-Leibler(KL)距离。该文分析了KL距离和Shannon不等式之间的关系,在此基础上,提出了一种新的算术-几何均值距离,并将这一距离测度用于多传感器遥感图像的配准处理。与Kullback-Leibler距离不同,新的距离测度具有对称性,并且对概率值为0的情况不需要特殊处理。文中首先通过一维仿真信号对算术-几何(AG)测度进行了分析,并使用Thematic Mapper (TM), Satellite POsitioning and Tracking (SPOT)遥感图像和雷达图像进行了配准实验,验证了提出的新的算术-几何均值距离函数在配准多传感器遥感图像方面的有效性。与目前常用的相关系数的方法不同,这种新方法对于像素灰度值不具有线性关系的多传感器遥感图像能够实现配准处理。     

Robust Multimodal Registration Using Local Phase-Coherence Representations

Automatic registration of multimodal images has proven to be a difficult task. Most existing techniques have difficulty dealing with situations involving highly non-homogeneous image contrast and a small initial overlapping region between the images. This paper presents a robust multi-resolution method for regis tering multimodal images using local phase-coherence representations. The proposed method finds the transformation that minimizes the error residual between the local phase-coherence representations of the two multimodal images. The error residual can be minimized using a combination of efficient globally exhaustive optimization techniques and subpixel-level local optimization techniques to further improve robustness in situations with small initial overlap. The proposed method has been tested on various medical images acquired using different modalities and evaluated based on its registration accuracy. The results show that the proposed method is capable of achieving better accuracy than existing multimodal registration techniques when handling situations where image non-homogeneity and small overlapping regions exist.     

融合显性和隐性度量的多模图像配准算法

为解决基于隐性度量的图像配准算法初始配准误差大容易引起失配的问题,本文提出了一种融合显性度量和隐性度量的多模图像分层配准算法。首先采用金字塔分解算法得到低分辨率待配准图像。然后在每一层金字塔图像中,先采用互信息作为显性度量,利用粒子群算法获得初始的配准参数;以此作为初始值,采用基于隐性度量的配准算法,利用融合粒子群和鲍威尔搜索法的优化算法获取更准确的配准参数。基于低分辨率图像计算得到配准参数后,先对高分辨率待配准图像进行变换,然后利用提出的上述算法进行参数优化,根据每层得到的配准参数计算最终的配准参数。最后,采用可见光与红外图像、多波段SAR图像进行了配准实验。实验结果表明,提出的算法适用于多模图像配准,能够减小配准误差,具有一定的适用性。      

An Automatic Image Registration for Applications in Remote Sensing

This paper deals with a major problem encountered in the area of remote sensing consisting of the registration of multitemporal and/or multisensor images. In general, such images have different gray-level characteristics, and simple techniques such as those based on correlation cannot be applied directly. In this work, a new automatic satellite image registration approach is proposed. This technique exploits the invariant relations between regions of a reference and a sensed image, respectively. It involves an edge-based selection of the most distinctive control points (CPs) in the reference image. The search for the corresponding CPs in the sensed image is based on local similarity detection by means of template matching according to a combined invariants-based similarity measure. The final warping of the images according to the selected CPs is performed by using the thin-plate spline interpolation. The procedure is fully automatic and computationally efficient. The proposed algorithm for this technique has been successfully applied to register multitemporal SPOT and synthetic aperture radar images from urban and agricultural areas. The experimental results demonstrate the robustness, efficiency and accuracy of the algorithm.     

基于特征的遥感图像自动配准算法

面向光学图像的多时相、多光谱、多传感器图像的自动配准,本文描述一个基于特征的高精度图像配准算法.它以点映射配准技术为基础,处理具有全局仿射几何失真的图像配准问题.首先,通过边缘检测和相应的后处理提取封闭边界;其次,根据边界链码相关和区域不变矩匹配策略建立边界的对应,并对对应重心即匹配点对进行一致性检测获得基元控制点;最后,估计初始变换参数,并通过显著点片的相关匹配来增加控制点个数,迭代修正变换参数以提高配准精度.多种遥感图像数据的配准实验和对比试验证实了的自动算法具有较高的可靠性和配准精度.     

一种CSS-SIFT复合图像配准算法

针对SIFT算法的图像配准耗时长的问题,提出一种CSS-SIFT复合图像配准算法。CSS-SIFT算法首先使用CSS算法检测图像特征,然后,使用优化的SIFT算法生成并降维图像特征描述子,最后,使用基于欧式距离和曼哈顿距离的优化双向匹配算法对图像特征进行匹配。仿真实验条件是通过计算机中仿真软件进行仿真实验,统计图像特征数目、匹配数目、正确匹配数目、配准准确率、配准时间与配准时间下降率共6个指标数据,统计结果表明,CSS-SIFT算法在图像配准准确度方面与传统SIFT算法、传统SURF算法、Forstern-SIFT算法、Harris-SIFT算法、Trajkovic-SIFT算法相当,但在图像配准耗时方面分别降低了58.45%、10.68%、14.84%、16.21%与4.63%,为图像配准提供了一种解决方案。     

Image Registration Using Adaptive Polar Transform

Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration, or analysis. Recently, researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, it suffers from nonuniform sampling which makes it not suitable for applications in which the registered images are altered or occluded. Inspired by LPT, this paper presents a new registration algorithm that addresses the problems of the conventional LPT while maintaining the robustness to scale and rotation. We introduce a novel adaptive polar transform (APT) technique that evenly and effectively samples the image in the Cartesian coordinates. Combining APT with an innovative projection transform along with a matching mechanism, the proposed method yields less computational load and more accurate registration than that of the conventional LPT. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Moreover an image comparison scheme is proposed for locating the area where the image pairs differ. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to occlusion and alteration in addition to scale, rotation, and translation.     

基于EM算法的联合数据关联与空间配准

针对雷达量测存在漏探测情况下的数据关联与空间配准问题,提出了一种基于期望最大化（EM）算法的联合数据关联与空间配准算法。该算法使用EM算法对空间配准问题中的系统偏差进行估计,采用多维分配算法进行数据关联,通过迭代进行数据关联和空间配准,最终得到收敛的系统偏差估计和关联正确率。仿真实验结果表明：在雷达探测为非全探测时,文中提出的联合数据关联与空间配准算法能够有效地估计系统偏差,有较强的抗噪性和鲁棒性。     

A VLSI Architecture for Image Registration in Real Time

Image registration is an ubiquitous task occurring in countless image analysis applications. A dedicated implementation of image registration algorithms is the best approach to meet the intensive computation requirements of implementing image registration schemes in real time. This paper presents an efficient VLSI architecture for real-time implementation of image registration algorithms using an exhaustive search method. Normalized cross correlation function, mean square error, and blue screen technique algorithms are implemented for image registration. The architecture is based on a data flow design that allows sequential inputs but performs parallel processing. Based on the architecture, a programmable chip can be designed for image registration. Chips can be cascaded to achieve better performance and sizes of both the search and the reference image which can vary with time from a small to a very large value.     

基于局部仿射配准的鲁棒非刚体配准算法

针对传统点集非刚体配准算法对复杂局部形变数据配准精度低,收敛速度慢等问题,该文提出一种基于局部仿射配准的鲁棒非刚体配准算法。该算法采用分层迭代的方式由粗到精地完成点集的非刚体配准。在每层迭代中,首先对子形状点集集合和子目标点集集合进行分块处理并更新分块后每一类子点集的形状控制点。然后利用控制点引导仿射迭代最近点(ICP)算法求解对应子点集间的局部仿射变换。接着利用上一步求解的局部仿射变换,更新子形状点集集合及其形状控制点集合。直到配准误差收敛时,循环结束并输出更新后的形状点集。实验结果表明,所提算法与传统点集非刚体算法相比具有更强的精确性和收敛性。     

Multimodality Image Registration Using Spatial Procrustes Analysis and Modified Conditional Entropy

In this paper, we propose a new image registration technique using two kinds of information known as object shapes and voxel intensities. The proposed approach consists of two registration steps. First, an initial registration is carried out for two volume images by applying Procrustes analysis theory to the two sets of 3D feature points representing object shapes. During this first stage, a volume image is segmented by using a geometric deformable model. Then, 3D feature points are extracted from the boundary of a segmented object. We conduct an initial registration by applying Procrustes analysis theory with two sets of 3D feature points. Second, a fine registration is followed by using a new measure based on the entropy of conditional probabilities. Here, to achieve the final registration, we define a modified conditional entropy (MCE) computed from the joint histograms for voxel intensities of two given volume images. By using a two step registration method, we can improve the registration precision. To evaluate the performance of the proposed registration method, we conduct various experiments for our method as well as existing methods based on the mutual information (MI) and maximum likelihood (ML) criteria. We evaluate the precision of MI, ML and MCE-based measurements by comparing their registration traces obtained from magnetic resonance (MR) images and transformed computed tomography (CT) images with respect to x-translation and rotation. The experimental results show that our method has great potential for the registration of a variety of medical images.