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。     

基于改进Keren配准方法的超分辨率算法

提出一种基于边缘检测和Keren配准方法的自适应归一化卷积超分辨率重建算法。为了进一步提高低分辨率序列图像间的配准精度,该算法将边缘检测与Keren配准算法相结合。首先利用Roberts算子对图像序列进行边缘检测,然后利用基于简化四参数仿射变换模型的Keren改进算法求出边缘图像间的平移和旋转参数。仿真实验结果表明即使在含有噪声及大角度旋转情况下,相比Keren改进算法该算法配准精度得到了显著提高;其中采用Roberts算子相比其他传统算子可获得更高的配准精度。最后采用自适应归一化卷积超分辨率融合算法进行超分辨率重建,真实混叠图像序列的实验表明,基于提出的这种配准方法的超分辨率重建图像获得了很好的视觉效果和更高的分辨能力,具有良好的应用价值。     

遗传算法和Powell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。     

蚁群算法和Powell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,成为近年来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.该文提出了一种蚁群算法和Powell法相结合的多分辨率搜索优化算法.该算法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,将蚁群算法与Powell法结合起来对三维的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互信息函数的局部极值,大大地提高了配准精度,达到亚像素级.     

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。     

基于GoogLeNet-WT-Canny的紫外成像仪中的图像配准与融合

针对目前紫外成像仪中紫外光图像与可见光图像配准叠加精度低、电晕放电定位偏差大等问题,提出一种基于GoogLeNet模型、小波变换(Wavelet Transform,WT)和Canny算子相结合的紫外与可见光图像配准融合方法,并将其应用于高灵敏紫外成像仪中.首先,引入迁移学习的思想,利用预训练的GoogLeNet模型自主挖掘可见光图像和紫外图像的特征;其次,将提取出来的特征作为预测变量,输入极限学习机(Extreme Learning Machine,ELM),以空间变换参数为指导监督模型训练,实现高精度紫外与可见光图像配准;最后,利用二维小波变换与Canny算子对配准后的图像进行多分辨率分析与边缘检测,实现无紫外信息损失的图像融合.实验结果表明,所提方法的紫外与可见光图像配准精度高,融合效果良好,具有很好的工程实用价值.     

基于改进SURF算子的高低分辨率图像配准方法

针对激光三维成像传感器与可见光传感器图像分辨率差异较大,配准过程中特征点误匹配情况严重的问题,提出了一种基于改进SURF算子的高低分辨率图像配准方法。首先,采用双线性插值算法对低分辨率图像进行预处理,然后在经典SURF算子的基础上,采用最近邻向量匹配法完成SURF特征的粗匹配,并基于特征偏移一致性原则对匹配情况做进一步优化,最后结合RANSAC和最小二乘法求出图像之间的仿射关系,利用所求的变换参数插值得到配准后的图像。实验结果表明,该配准方法在保持配准速度的同时,结构相似性测量指数提高了约11%,进一步提高了配准的精度。     

亚像元图像超分辨率重建研究

针对光纤束耦合成像系统的特点,对存在亚像元量级空间错位的图像进行超分辨率重建研究。研究分为图像配准与融合两个步骤。图像配准时,用经典的Keren配准算法对两幅图像进行亚像元级别配准。图像融合时结合双三次插值对配准后两幅图像进行Mallat小波分解与重构,从而实现图像超分辨率重建。实验结果表明,简化后的配准算法配准精度达到了0.01像素,而经过插值与融合重建出来的图像空间分辨率提高了一倍,并且保留了丰富的细节信息。     

SAR图像多尺度配准的小波域最小割模型方法

 针对合成孔径雷达(Synthetic Aperture Radar,简称SAR)图像提出了一种基于小波域改进最小割(Improved Minimum Cut,简称IMC)模型的多尺度配准方法.该方法首先将小波分解后的高频信息与SAR图像的特性相结合,用以指导在低频子图像中基于IMC模型的图像分割,从而获得两幅图像中完整的区域轮廓特征,然后通过融合开闭轮廓的信息,分别利用统计直方图和Hausdorff距离获取配准参数并对之细化,最后通过逐层校正参数完成多尺度的配准.实验结果表明,该方法能避免斑点噪声对轮廓提取和匹配的影响,能够实现具有平移、尺度和旋转变化的亚像素级精度的SAR图像配准.     

一种改进的基于互信息的自动图像配准算法

提出了改进图像配准的基于互信息的算法,首先将参考图像和待配准图像分解到小波域并二值化,以归一化互信息作为相似性测度进行配准.配准参数搜索作用SPSA算法和鲍威尔算法相结合的优化策略.实验证明本算法可以快速、精确且不需要人工干预地实现两幅图像的配准.     

同时偏振成像探测系统的偏振图像配准研究

同时偏振成像探测技术是一种新型的偏振成像探测技术,它能在同一个探测器上同时获得被探测目标0、45、90、135 4个偏振方向的偏振强度图像。为了准确获取被探测目标的偏振信息,这４幅图像的配准显得尤为重要。为了使配准精度达到0.1个像元,结合同时偏振成像探测系统的成像方式,提出了一种基于空域和频域互相关的偏振图像配准方法。该方法使用硬件和软件相结合的方式完成图像的配准。首先使用空域互相关的算法实现图像的像元级粗配准;然后使用频域互相关的像元级配准算法进行像元级精配准;最后使用频域互相关的亚像元配准算法实现偏振图像的亚像元级配准。     

Voxel-based 2-D/3-D registration of fluoroscopy images and CT scansfor image-guided surgery

Registration of intraoperative fluoroscopy images with preoperative 3D CT images can he used for several purposes in image-guided surgery. On the one hand, it can be used to display the position of surgical instruments, which are being tracked by a localizer, in the preoperative CT scan. On the other hand, the registration result can be used to project preoperative planning information or important anatomical structures visible in the CT image on to the fluoroscopy image. For this registration task, a novel voxel-based method in combination with a new similarity measure (pattern intensity) has been developed. The basic concept of the method is explained at the example of 2D/3D registration of a vertebra in an X-ray fluoroscopy image with a 3D CT image. The registration method is described, and the results for a spine phantom are presented and discussed. Registration has been carried out repeatedly with different starting estimates to study the capture range. Information about registration accuracy has been obtained by comparing the registration results with a highly accurate “ground-truth” registration, which has been derived from fiducial markers attached to the phantom prior to imaging. In addition, registration results for different vertebrae have been compared. The results show that the rotation parameters and the shifts parallel to the projection plane can accurately be determined from a single projection. Because of the projection geometry, the accuracy of the height above the projection plane is significantly lower     

Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation

For patient setup verification in external beam radiotherapy (EBRT) of prostate cancer, we developed an information theoretic registration framework, called the minimax entropy registration framework, to simultaneously and iteratively segment portal images and register them to three-dimensional (3-D) computed tomography (CT) image data. The registration framework has two steps, the max step and the min step, and evaluates appropriate entropies to estimate segmentations of the portal images and to find the transformation parameters. In the initial version of the algorithm (Bansal et al. 1999), we assumed image pixels to be independently distributed, an assumption not true in general. Thus, to better segment the portal images and to improve the accuracy of the estimated registration parameters, in this initial formulation of the problem, the correlation among pixel intensities is modeled using a one-dimensional Markov random process. Line processes are incorporated into the model to improve the estimation of segmentation of the portal images. In the max step, the principle of maximum entropy is invoked to estimate the probability distribution on the segmentations. The estimated distribution is then incorporated into the min step to estimate the registration parameters. Performance of the proposed framework is evaluated and compared to that of a mutual information-based registration algorithm using both simulated and real patient data. In the proposed registration framework, registration of the 3-D CT image and the portal images is guided by an estimated segmentation of the pelvic bone. However, as the prostate can move with respect to the pelvic structure, further localization of the prostate using ultrasound image data is required, an issue to be further explored in future.     

Registration of 3-D CT and 2-D Flat Images of Mouse via Affine Transformation

It is difficult to directly coregister the 3-D fluorescence molecular tomography (FMT) image of a small tumor in a mouse whose maximal diameter is only a few millimeters with a larger CT image of the entire animal that spans about 10 cm. This paper proposes a new method to register 2-D flat and 3-D CT image first to facilitate the registration between small 3-D FMT images and large 3-D CT images. A novel algorithm combining differential evolution and improved simplex method for the registration between the 2-D flat and 3-D CT images is introduced and validated with simulated images and real images of mice. The visualization of the alignment of the 3-D FMT and CT image through 2-D registration shows promising results.      

3-D/2-D registration by integrating 2-D information in 3-D

In image-guided therapy, high-quality preoperative images serve for planning and simulation, and intraoperatively as "background", onto which models of surgical instruments or radiation beams are projected. The link between a preoperative image and intraoperative physical space of the patient is established by image-to-patient registration. In this paper, we present a novel 3-D/2-D registration method. First, a 3-D image is reconstructed from a few 2-D X-ray images and next, the preoperative 3-D image is brought into the best possible spatial correspondence with the reconstructed image by optimizing a similarity measure (SM). Because the quality of the reconstructed image is generally low, we introduce a novel SM, which is able to cope with low image quality as well as with different imaging modalities. The novel 3-D/2-D registration method has been evaluated and compared to the gradient-based method (GBM) using standardized evaluation methodology and publicly available 3-D computed tomography (CT), 3-D rotational X-ray (3DRX), and magnetic resonance (MR) and 2-D X-ray images of two spine phantoms, for which gold standard registrations were known. For each of the 3DRX, CT, or MR images and each set of X-ray images, 1600 registrations were performed from starting positions, defined as the mean target registration error (mTRE), randomly generated and uniformly distributed in the interval of 0-20 mm around the gold standard. The capture range was defined as the distance from gold standard for which the final TRE was less than 2 mm in at least 95% of all cases. In terms of success rate, as the function of initial misalignment and capture range the proposed method outperformed the GBM. TREs of the novel method and the GBM were approximately the same. For the registration of 3DRX and CT images to X-ray images as few as 2-3 X-ray views were sufficient to obtain approximately 0.4 mm TREs, 7-9 mm capture range, and 80%-90% of successful registrations. To obtain similar results for MR to X-ray registrations, an image, reconstructed from at least 11 X-ray images was required. Reconstructions from more than 11 images had no effect on the registration results.     

2D/3D registration and motion tracking for surgical interventions

In order to use pre-operative images during an intervention for navigation, they must be registered to the patient's co-ordinate system in the operating theatre or to an intra-operative image. For the registration to be valid in the case of patient movements, the registration must be updated or the patient movement must be tracked. One problem in this area is the registration of intra-operatively acquired X-ray fluoroscopies with 3D CT images obtained before the intervention as well as motion tracking for this setup. The result can be used to support the placement of pedicle screws in spine surgery or aortic endoprostheses in transfemoral endovascular aneurysm management (TEAM). The different approaches to 2D/3D registration are discussed and a novel voxel-based method is presented: using a small part of the CT image covering only the vertebra of interest, pseudo-projections are computed and the resulting vertebra template is compared to the X-ray projection using a new similarity measure which is called pattern intensity. Application, performance and registration accuracy are discussed and demonstrated by application to images of a TEAM procedure and of a spine phantom.     

Multimodality image registration by maximization of mutualinformation

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications     

Likelihood maximization approach to image registration

A likelihood maximization approach to image registration is developed in this paper. It is assumed that the voxel values in two images in registration are probabilistically related. The principle of maximum likelihood is then exploited to find the optimal registration: the likelihood that given image f, one has image g and given image g, one has image f is optimized with respect to registration parameters. All voxel pairs in the overlapping volume or a portion of it can be used to compute the likelihood. A knowledge-based method and a self-consistent technique are proposed to obtain the probability relation. In the knowledge-based method, prior knowledge of the distribution of voxel pairs in two registered images is assumed, while such knowledge is not required in the self-consistent method. The accuracy and robustness of the likelihood maximization approach is validated by single modality registration of single photon emission computed tomographic (SPECT) images and magnetic resonance (MR) images and by multimodality registration (MR/SPECT). The results demonstrate that the performance of the likelihood maximization approach is comparable to that of the mutual information maximization technique. Finally the relationship between the likelihood approach and the entropy, conditional entropy, and mutual information approaches is discussed.     

基于级联变换的光学和SAR图像配准算法

针对光学和SAR(Synthetic Aperture Radar)图像配准中存在明显辐射和几何差异的问题,提出了一种基于级联变换的多源遥感图像配准方法.首先,利用灰度变换提取光学和SAR图像间的稳定结构特征,去除辐射差异性;然后,提出一种新的加权对数极坐标变换算法,解决图像间全局几何差异性,保证算法的尺度和旋转不变性,并初步得到整体的平移量;最后,通过局部几何变换,得到一系列的匹配点对,构建薄板样条模型,实现图像的精确配准.实验验证了算法去除辐射差异性和获得全局几何变换参数的能力,与传统的多源图像配准算法相比,基于级联变换的配准算法鲁棒性好,配准精度高.     

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。