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。     

基于抽样随机一致性和图像特征的图像配准

文中介绍了一种基于Harris角点检测图像特征的图像配准方法。通过随机一致性算法(RANSAC)和正则化交叉相关测度建立了待配准图像和模板图像之间特征点的对应关系。分别采用仿射变换和透射变换通过该对应关系进行图像配准。在完成配准后,分别采用自适应B样条插值算法和多项式插值算法对配准后的图像进行校正。采用信噪比和交叉相关系数作为评价标准,通过对几种方法相结合的图像处理结果进行比较,得出结论:对应超声TOFD图像,仿射变换结合B样条插值校正的方法可以得到最好的效果。     

基于逆拉伸变换的脑血管减影图像非刚性配准

提出了一种基于逆拉伸空间变换的蒙片像素点灰度值映射求解方法。实验中使蒙片网格化以产生恰当的控制点，采用最大减影直方图能量相似性测度准则和鲍威尔优化搜索方法，对蒙片图像和活片图像进行局部变形非刚性配准。该算法有效保证了蒙片图像变形前后灰度值变化的拓扑一致，使变形后的蒙片图像仍保持清晰准确的轮廓，配准后的减影图像质量良好，噪声和伪影明显减少，同时省去插值计算过程，配准速度明显提高，为临床中的脑血管病变诊断提供了有力支持。     

基于互信息量和薄板样条的X射线造影图像弹性配准

本文提出了一种基于互信息量和薄板样条的冠状动脉造影图像弹性配准新方法.该方法不仅解决了传统模板匹配方法中子块图像分解的不确定性问题,而且还克服了常规优化方法易受初值影响而陷入局部最优的问题,有效的保证了解的收敛.试验结果证明本算法能够获得亚像素的配准精度.     

基于逆拉伸变换的脑血管减影图像非刚性配准

提出了一种基于逆拉伸空间变换的蒙片像素点灰度值映射求解方法.实验中使蒙片网格化以产生恰当的控制点,采用最大减影直方图能量相似性测度准则和鲍威尔优化搜索方法,对蒙片图像和活片图像进行局部变形非刚性配准.该算法有效保证了蒙片图像变形前后灰度值变化的拓扑一致,使变形后的蒙片图像仍保持清晰准确的轮廓,配准后的减影图像质量良好,噪声和伪影明显减少,同时省去插值计算过程,配准速度明显提高,为临床中的脑血管病变诊断提供了有力支持.     

医学图像配准中的数据抽样方法研究

针对在基于灰度的医学图像配准中,传统数据抽样方法在过抽样时产生较多的局部极值点问题,提出了基于浮动图像灰度概率分布和其梯度信息的抽样方法.通过对人体脑部的刚体配准实验,从函数曲线和收敛性能方面,对比分析了五种数据抽样方法.实验结果表明,新抽样方法可以有效地减少局部极值点,提高归一化互信息测度的收敛性能.     

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...     

基于区域选择和特征点匹配的图像配准算法

提出了一种新的基于圆形区域和Harris角点检测的图像配准算法.该方法采用Harris算子检测特征点信息,自动选取有效特征点,充分利用圆形区域的旋转不变性和互信息量最大原则进行特征点匹配,避免了传统的图像配准算法计算数据量过大、特征点匹配不准确等问题.仿真结果表明,该方法能在27.8 s内完成配准过程,优于传统的图像配准方法;在旋转角度上有0.07°的误差,但并不影响平移距离的正确配准.     

基于小波和改进型Hausdorff距离的遥感图像配准方法

近年来,不断发展的遥感技术使传统的遥感图像配准方法显得难以胜任.本文介绍了一种准确、高效的遥感图像配准方法.该方法运用图像信息分层技术、小波多尺度图像分解和Harris角点检测算子进行特征点提取,构建一个特征点金字塔,利用改进型Hausdorff距离作为配准的相似性测度,从而实现遥感图像的配准.     

基于曲线拟合的干涉合成孔径雷达(InSAR)复图像自动配准算法

由于乘性噪声和相干斑的存在,干涉合成孔径雷达(InSAR)复图像对的配准算法比传统的图像配准算法更困难.该文提出了一种基于B-样条曲线拟合和匹配的InSAR复图像对的自动配准算法,能够有效地配准InSAR复图像对.在利用最小二乘法建立配准参数后,应用两步法完成了亚像元级配准.实验结果表明,该算法具有较高的精度和稳健性.     

Fast parametric elastic image registration

We present an algorithm for fast elastic multidimensional intensity-based image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard real-world problems, it is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. Particular attention has been paid to the factors influencing the speed of this algorithm. The B-spline deformation model is shown to be computationally more efficient than other alternatives. The algorithm has been successfully used for several two-dimensional (2-D) and three-dimensional (3-D) registration tasks in the medical domain, involving MRI, SPECT, CT, and ultrasound image modalities. We also present experiments in a controlled environment, permitting an exact evaluation of the registration accuracy. Test deformations are generated automatically using a random hierarchical fractional wavelet-based generator.     

Elastic registration of biological images using vector-spline regularization

We present an elastic registration algorithm for the alignment of biological images. Our method combines and extends some of the best techniques available in the context of medical imaging. We express the deformation field as a B-spline model, which allows us to deal with a rich variety of deformations. We solve the registration problem by minimizing a pixelwise mean-square distance measure between the target image and the warped source. The problem is further constrained by way of a vector-spline regularization which provides some control over two independent quantities that are intrinsic to the deformation: its divergence, and its curl. Our algorithm is also able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when its repartition is uneven. We provide an optimal analytical solution in the case when only landmarks and smoothness considerations are taken into account. We have applied our approach to perform the elastic registration of images such as electrophoretic gels and fly embryos. The validation of the results by experts has been favorable in all cases.     

Image registration by "Super-curves"

We solve the 2-D affine image registration problem by curve matching and alignment. Our approach starts with a super-curve, which is formed by superimposing two affine related curves in one coordinate system. We use B-spline fusion technique to find a single B-spline approximation of the super-curve and a registration between the two curves simultaneously. This approach achieves superior accuracy and efficiency in curve matching and alignment. We then address the occlusion problem by finding partial match between the curves segmented using inflections and cusps, which are affine invariant. The combination of edge detection and curve alignment lead to accurate image registration.     

Landmark-based elastic registration using approximating thin-platesplines

We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. This approach is an extension of the original interpolating thin-plate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. Our approach is based on a minimizing functional and can cope with isotropic as well as anisotropic landmark errors. In particular, in the latter case it is possible to include different types of landmarks, e.g., unique point landmarks as well as arbitrary edge points. Also, the scheme is general with respect to the image dimension and the order of smoothness of the underlying functional. Optimal affine transformations as well as interpolating thin-plate splines are special cases of this scheme. To localize landmarks we use a semi-automatic approach which is based on three-dimensional (3-D) differential operators. Experimental results are presented for two-dimensional as well as 3-D tomographic images of the human brain.     

Coupled B-snake grids and constrained thin-plate splines foranalysis of 2-D tissue deformations from tagged MRI

Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization and create tagged patterns within a deforming body such as the heart muscle. The resulting patterns define a time-varying curvilinear coordinate system on the tissue, which the authors track with coupled B-snake grids. B-spline bases provide local control of shape, compact representation, and parametric continuity. Efficient spline warps are proposed which warp an area in the plane such that two embedded snake grids obtained from two tagged frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the plane, where no information is available, a C¹ continuous vector field is interpolated. The implementation proposed in this paper improves on the authors' previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The methods are validated with a cardiac motion simulator, in addition to in-vivo tagging data sets     

Mean Shift的渐进无偏变换图像配准

基于标志点对应关系的图像配准方法不能保证变换的无偏性,一般不适用于大形变配准问题.该文提出了一种基于标志点对应关系的渐进式无偏变换图像配准方法,该方法利用少量的初始标志点对,通过Mean Shift迭代寻找对应标志点对,构造无偏性较好的变换函数,逐步得到一致的配准结果.文中详细讨论了标志点的分布对变换函数无偏性的影响,...     

Automatic matching of homologous histological sections

The role of neuroanatomical atlases is undergoing a significant redefinition as digital atlases become available. These have the potential to serve as more than passive guides and to hold the role of directing segmentation and multimodal fusion of experimental data. Key elements needed to support these new tasks are registration algorithms. For images derived from histological procedures, the need is for techniques to map the two-dimensional (2-D) images of the sectional material into the reference atlas which may be a full three-dimensional (3-D) data set or one consisting of a series of 2-D images. A variety of 2-D-2-D registration methods are available to align experimental images with the atlas once the corresponding plane of section through the atlas has been identified. Methods to automate the identification of the homologous plane, however, have not been previously reported. Here, the authors use the external section contour to drive the identification and registration procedure. For this purpose, the authors model the contours by B-splines because of their attractive properties the most important of which are: (1) smoothness and continuity; (2) local controllability which implies that local changes in shape are confined to the B-spline parameters local to that change; (3) shape invariance under affine transformation, which means that the affine transformed curve is still a B-spline whose control points are related to the object control points through the transformation. The authors present a fast algorithm for estimating the control points of the B-spline which is robust to nonuniform sampling, noise, and local deformations. Curve matching is achieved by using a similarity measure that depends directly on the parameters of the B-spline. Performance tests are reported using histological material from rat brains     

基于最大化对齐度的多模态图像自动配准

该文讨论图像配准的广义定义并提出一种新的图像配准准则一对齐度．对齐度能够正确地反映两幅不同灰度属性的图像是否配准，并且将图像配准归结为对齐度的最大化．新准则可以用来配准多模态图像包括灰度属性差异很大的图像．该文应用多分辨率方法和Powcll方向族搜索法来最大化对齐度．因此，用该方法可以自动地处理图像配准问题，不需要定义地标点或特征点．多模态医学图像和遥感图像的自动配准的例子说明了该文方法的有效性。     

A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.