Image registration

In order to demonstrate the growth of the medical image registration field over the past decades, this paper presents the number of journal publications on this topic since 1988 until 2002. In a similar manner, trends in topics within the field of medical image registration are detected. Publications on computed tomography (CT) and magnetic resonance imaging (MRI) are rather constant through the years. Positron emission tomography (PET) and single photon emission computed tomography (SPECT), on the other hand, seem to loose ground to newly emerging functional imaging techniques, such as functional MRI (fMRI) whereas an increase in interest in registration of ultrasound (US) images was observed. Two topics in image registration that are currently considered hot are intraoperative and elastic registration. Although the interest in intraoperative registration strongly increased in the late 1990s, there seems to be a slight relative decrease in recent years. On the other hand, elastic registration has become a popular topic, reaching the highest numbers so far in 2002.     

Image registration by recognition of corresponding structures

A method for automatic image registration which is characterized by its insensitivity to scaling, rotation, and intensity changes is described. The method is based on similarity assessment of the structures in the images and on a check of their spatial arrangement. Pairs of structures that correspond to each other provide sets of control points to geometric mapping functions. An application of the method to remote-sensing image alignment with a reference map is presented     

Image registration using Fermat transforms

Binary boundary maps are obtained by edge detection, then their relative translation is determined by conventional cross-correlation computed by the Fermat transform. Use of boundary maps emphasises edges and sharp discontinuities, while rejecting disturbances related to nonuniform illumination, thus avoiding the costly normalisation step. Use of integer arithmetic allows significant savings in computation     

Image registration for image-based rendering.

Image-based rendering (IBR) has received much attention in recent years for its ability to synthesize photo-realistic novel views. To support translational motion, existing IBR methods either require a large amount of reference images or assume that some geometric information is available. However, rendering with a large amount of images is very expensive in terms of image acquisition, data storage, and memory costs. As IBR accepts various kinds of geometric proxy, we may use image registration techniques, such as stereo matching and structure and motion recognition, to obtain geometric information to help reduce the number of images required. Unfortunately, existing image registration techniques only support a small search range and require closely sampled reference images. This results in a high spatial sampling rate, making IBR impractical for use in scalable walkthrough environments.Our primary objective of this project is to develop an image registration technique that would recover the geometric proxy for IBR while, at the same time, reducing the number of reference images required. In this paper, we analyze the roles and requirements of an image registration technique for reducing the spatial sampling rate. Based on these requirements, we present a novel image registration technique to automatically recover the geometric proxy from reference images. With the distinguishing feature of supporting a large search range, the new method can accurately identify correspondences even though the reference images may only be sparsely sampled. This can significantly reduce the acquisition effort, the model size, and the memory cost.     

基于SIFT的图像配准方法

针对大尺度图像配准和不同传感器图像配准问题,介绍了一种基于SIFT的图像配准方法。首先提取图像中适应尺度变化的不变特征点,在提取过程中加入多尺度Harris检测算子,提高了匹配点对的重复率,通过聚类和归一化互信息准则对候选匹配点对的角度、尺度和位置特征进行迭代筛选,删除错误的匹配点对,最后得到正确的匹配点对,对图像进行配准。实验结果表明：该方法能处理相似变换的图像配准。     

基于SURF的图像配准方法研究

图像配准在计算机视觉、医学诊断与治疗、图像拼接等领域有广泛的应用.基于特征的方法具有压缩信息量、执行速度快、精度高等优点,成为近年来研究的热点,SIFT是其中之一.但传统的SIFT方法数据量大、计算耗时长,提出了一种基于SURF的图像配准方法.首先用SURF方法提取特征点,其次用最近邻匹配法找出对应匹配点对,结合RANSAC和最小二乘法求出图像之间的映射关系,最后利用所求的变换参数插值得到配准后的图像.实验表明:该配准算法既满足参数估算准确的要求,又具有比SIFT计算量小、速度快的优点,有一定的理论和应用价值.     

Image registration based on boundary mapping

A new two-stage approach for nonlinear brain image registration is proposed. In the first stage, an active contour algorithm is used to establish a homothetic one-to-one map between a set of region boundaries in two images to be registered. This mapping is used in the second step: a two-dimensional transformation which is based on an elastic body deformation. This method is tested by registering magnetic resonance images to atlas images.     

Image registration by maximization of combined mutual information and gradient information

Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations. Results of combining both standard mutual information as well as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.     

Image registration using the Walsh transform.

This paper presents a new algorithm which can be used to register images of the same or different modalities, e.g., images with multiple channels, such as X-rays, temperature or elevation, or simply images of different spectral bands. In particular, a correlation-based scheme is used, but instead of gray values, it correlates numbers formulated by different combinations of the extracted local Walsh coefficients of the images. Each image patch is expanded in terms of Walsh basis functions. Each Walsh basis function can be thought of as measuring a different aspect of local structure, e.g., horizontal edge, corner, etc. The coefficients of the expansion, therefore, can be thought of as dense local features, estimating at each point the degree of presence of, for example, a horizontal edge, a corner with contrast of a certain type, etc. These coefficients are normalized and used as digits in a chosen number system which allows one to create a unique number for each type of local structure. The choice of the basis of the number system allows one to give different emphasis to different types of local feature (e.g., corners versus edges), and, thus, the method we present forms a unified framework in terms of which several feature matching methods may be interpreted. The algorithm is compared with wavelet and contour based approaches, using simulated and real images. The two images are assumed to differ from each other by a rotation and a translation only.     

基于MATLAB图像配准方法的相关研究

图像配准是图像融合等其他图像处理应用的前提,是目前图像处理中的热点。在信息高度化的今天,该技术已经渗透到各个方面,被广泛地应用在遥感图像、医学图像、三维重构等诸多领域中,在图像分析和处理中占有举足轻重的作用。本文主要介绍了利用MATLAB提供的图像处理工具箱（IPT）,来方便快捷地完成图像之间的配准。文中首先对图像配准及基于点特征的图像配准的概念作了比较详细的介绍,然后对两幅图像在MATLAB平台进行了配准操作,最后对图像处理的结果进行了相关的分析,阐述了该方法的可取之处和有待改进之处。     

基于改进SRS-RANSAC图像配准算法

针对目前图像配准算法存在的配准时间较长、配准正确率低等问题,本文提出一种基于改进分层随机选择一致性(Stratified Random Selection Random Sample Consensus,SRS-RANSA)的图像配准算法.首先,通过ORB(Oriented FAST and Rotated BRIEF)算法对参考图像进行特征点提取;其次,采用最小距离法初步过滤匹配中存在的误匹配数量;最后,随机抽样一致性(RANSAC)框架中通过分层随机选择(SRS)提取分布相对分散且均匀的特征点,进一步过滤掉初始匹配中存在的不匹配特征点,实现提高配准正确率的同时缩短运行时间.通过本文算法与其他算法在Oxford标准图集和现实中拍摄的图像进行实验对比,结果表明,本文算法在匹配正确率与运行效率上有所提高.     

面向引线框架缺陷的图像配准算法

为了降低引线框架缺陷识别的误检率,比较了基于灰度的模板匹配和基于特征的匹配算法之间的优缺点,并针对引线框架缺陷检测中参考图和检测图存在差异的特点,提出基于区域定位和不变矩的特征匹配算法。该算法通过边缘检测定位出特征区域,并用不变矩进行区域特征描述。在缺陷识别试验中,相比于模板匹配算法,该算法表现出更快的结算速度,更高的配准精度,更低的配准失败概率。结果表明,该配准算法适用于引线框架缺陷识别,降低误检率。     

基于和声算法的图像配准技术

和声搜索算法是新近问世的一种搜索算法,在许多实际问题中都得到了成功应用。和声搜索算法通过不断调整修改记忆库中的解变量和不同的取值概率、微调概率,从而使函数值随着迭代次数的增加不断收敛,达到优化的完成。算法本身可调参数少、且容易实现。引入和声搜索算法解决图像配准问题,将和声算法应用于配准图像中。实验结果通过互信息大小表明,和声搜索算法在一定程度上可以很好地达到对图像进行配准的目的。     

基于改进自适应遗传算法的图像配准方法

采用遗传算法进行图像配准时,存在收敛速度慢、易早熟的问题,可能导致误配。为克服这些缺点,提出了改进的自适应遗传算法(improved adaptive genetic algorithm,IAGA)。该算法以互信息作为相似性测度,通过对遗传参数设置的改进,自适应的调解进化过程中的交叉概率和变异概率,既提高了遗传算法的收敛速度,又有效地防止了早熟。实验结果表明,改进算法具有更好的有效性和精确性。     

基于稀疏方位超图匹配的图像配准算法

为提高超图匹配的正确匹配率并降低其计算复杂度,提出了一种基于稀疏方位超图匹配的图像配准算法。提取图像的结构特征点为图节点,采用最小生成树算法获取节点间的主要连接关系,并用包含邻近的节点与边的三元组结构定义超边,计算超边的方位角度信息,由此构建稀疏方位超图;利用方位信息构建亲近矩阵,并采用全局最优匹配方法实现匹配。实验表明,对于实际图像的配准,该算法既具有较低的计算复杂度,又有良好的匹配效果。     

融入曲率尺度空间算法的图像配准方法

为了满足图像配准对于实时性的要求,提出融入曲率尺度空间算法的图像配准方法.首先使用曲率尺度空间算法提取图像角点特征,然后使用改进的加速稳健特征算法生成64维角点特征描述子向量并将描述子向量降维到24维,最后使用改进的相似性距离算法和随机采样一致性算法进行匹配.仿真实验一表明:在图像配准准确度方面与传统尺度不变特征变换算...     

基于边缘特征和相位相关的图像配准

由于拍摄时间不同、传感器的抖动等原因,成像系统产生的序列图像之间具有一定的位移矢量。研究了一种基于边缘和相位相关的图像配准方法,首先提取图像边缘,然后进行归一化相位相关,从而估算出序列图像之间的位移量。实验结果表明,该方法的配准精度明显优与传统的相位相关方法,并对噪声具有较强的抑制能力。     

Image registration under illumination variations using region-based confidence weighted M-estimators

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M -estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.     

基于云核粒子群算法的图像配准研究

针对图像配准中存在的问题,提出云核粒子群算法.首先在云模型中采用正向正态云发生器,实现了在定性信息中获得定量的范围和分布规律;接着云滴将粒子群体分成三个子群,每个群由不同的云核进行引导,更新粒子位置与速度,云核粒子在引领子群内所有粒子到历史最佳位置的欧几里得空间距离的最大值为影响半径,对于在子群中影响半径以外的粒子,粒子核不再起引导作用;最后把图像配准过程中目标函数达到最小值问题转化为云核粒子群算法寻优问题,实验仿真得出配准后图像的满足视觉要求,位置与参考图像保持了一致.