基于Hausdorff距离图象配准方法研究

图象配准是图象融合的一个重要步骤，为此提出了一种自动图象配准算法，该算法从两幅待配准的图象中分别抽取特征点，然后选用Hausdorff距离对两特征点集进行匹配，得到点集间的仿射变换，从而实现图象的自动配准，此算法以特征点而不是物体边缘计算仿射变换，大大降低了计算Hausdorff距离的运算量；同时，基于Hausdorff距离的图象匹配只需要点集之间的对应，而无须点与点的对应，因而可以使用于存在较大物体形变的情况，即完成两幅差异较大图象的配准，实验结果证明了算法的有效性。     

形变医学图像配准方法设计与仿真

利用目前方法对形变医学图像进行配准时，没有提取形变医学图像特征，存在特征点获取结果与实际结果相差大、医学图像配准效果差和医学图像配准时间长的问题。为此提出基于角点检测与SIFT的形变医学图像配准方法。采用角点检测与SIFT相结合的方法对医学图像的特征点进行提取，在图像特征提取前，优先对尺度空间的极值点进行检测，其次生成角点特征，通过检测结果与最终特征点的方向完成医学图像特征点的提取，提升了医学图像配准精度。将提取的特征输入到构建的深度学习模型中，根据提取特征的训练及损失函数的优化实现形变医学图像配准。实验结果表明，通过对上述方法进行特征点获取结果与实际结果对比测试、医学图像配准效果测试和配准时间测试，验证了上述方法的准确性与有效性。     

图像配准的复制动力学方法

为了实现高精度图像配准,把单个像素看做参与博弈的局中人,将图像配准视为图像中各个像素与其周边像素的演化博弈过程。为了寻求博弈的均衡点,根据配准问题的特点,对复制方程进行修改,提出基于修改后的复制方程的图像配准方法。新方法是完全基于问题域建立起来的,能实现对任意形变的高精度配准,并且配准精度与分辨率无关。针对刚性形变的遥感图像和非刚性形变的医学图像进行了两组实验,结果表明了新算法的有效性。     

下颌骨CT序列图象的三维重建

在对医学图象三维重建的研究现状和已有的重建方法进行分析的基础上，从牙科医生进行牙种植手术的实际情况出发，提出了一种简单，实用的三维图象重建算法－影像配准叠加法，该方法的基本思路是：首先对二维CT数字图象进行影像纠正，内插和配准，以确定每个像素的三维坐标值，再按其空间位置关系给每个像素点设置适当的透明度，然后进行影像配准叠加，以便在二维屏幕上显示出具有立体感的真三维图象来，实验结果表明，该方法定位精度高，重建图象失真度小，既能作定性观察，又能作定量测量，并且测量数据精度较高。     

基于最大互信息和相关法的医学图象配准

本文提出了一种医学图象配准的方法,该方法分两步第一步采用搜索最大互信息的方法对图象做粗配准,使得参考图象与待配准图象大致一致;第二步采用快速相关法对图象做精细配准,从而获得医学图象的最优配准.由于此种算法具有良好的扩展性,所以既可用于多模医学图象的配准,也可用于单模医学图象的配准.     

基于VoxelMorph的可见光和红外遥感影像配准

由于可见光和红外的成像机理、成像波段不同,获取的遥感影像之间存在复杂的非线性辐射畸变,传统的配准方法难以实现两者的高精度配准。本文提出一种基于VoxelMorph的可见光和红外遥感影像配准方法,利用卷积神经网络对可见光和红外异源图像进行分步的精细化形变场计算,从而实现快速高精度配准。将可见光图像作为参考图像,利用U-Net网络计算待配准红外图像和参考（可见光）图像的形变场,实现全局对齐的仿射变换,然后通过空间转换网络进一步实现更高自由度变形。采用WHU-OPT-SAR数据集的实验结果表明,与基于尺度不变特征变换（SIFT）算法的传统配准方法相比,本文提出的基于VoxelMorph配准方法可以获得更好的配准效果,验证了基于VoxelMorph的配准方法在多源遥感影像领域的有效性。     

基于Legendre矩的CT及MR医学图象融合方法

为了提高CT、MR多模态医学图象配准、融合的精度和速度,提出了基于Legendre矩的CT和MR多模态医学图象配准、融合方法,并运用二维9数据图象的Legendre矩正交性和无冗余性的特点,通过找出CT及MR两种模态医学图象的质心,计算出两图象的比例因子,从而完成了两图象的平移和旋转,并精确地实现了CT和MR两模态图象的配信、融合,还优化了Legendre矩的快速算法和提高了应用Legendre矩配准CT和MR图象的速度。实验表明,利用Legendre矩对CT和MR等多模态图象配准、融合,不失为一种比较直接、简洁的方法;同时,Legendre矩在医学影象诊断、放疗计划系统等方面也具有重要的应用价值。     

遥感图像配准的稳健投影非负矩阵分解方法

由于要配准的目标存在可能的形变,震前和震后遥感图像的配准变得很困难。为了解决这个问题,提出基于稳健的投影非负矩阵分解（RPNMF）的配准方法来精确的配准形变目标。给出一种稳健的投影非负矩阵分解方法来获得震前震后形变目标的共同投影空间,利用在共同投影空间的投影来配准形变目标。为验证该算法的有效性,做了两个实验：2008年5月12日汶川地震前后的SAR图像的配准;唐家山堰塞湖的变化检测。与现有方法进行比较,结果表明该方法能够有效地得到形变目标的共同投影空间,并取得了很好的配准结果;同时,堰塞湖的变化检测也得到了很好的结果。     

基于空间加权互信息的非刚性医学图像配准

互信息作为相似性测度标准在医学图像配准领域应用广泛,尤其是在刚性配准研究中,能实现较高配准精度和非常稳健的配准效果。然而由于非线性形变的复杂性,在非刚性配准领域,对基于互信息的配准方法进行深入研究势在必行。提出的方案采用基于B样条的自由变换模型模拟医学图像中解剖结构的非线性形变,同时根据变换模型,考虑空间信息对配准效果的影响,以空间加权的方式对互信息方法进行改进。使用对大规模参数优化效果较好的LBFGS方法对配准参数进行优化,编程实现程序,并通过实验对效果进行验证和分析。实验结果显示,该方案配准精度明显优于传统的互信息方法。     

混合样条大尺度医学图像弹性配准算法研究*

针对大尺度形变医学图像配准速度慢和精度低的特点,提出一种结合薄板样条(TPS)和B样条的弹性配准方法。该方法采用尺度不变特征变换算法(SIFT)进行图像特征提取与匹配,利用TPS算法将特征点对作为输入进行预处理,以降低浮动图像的形变尺度,从而提高下一步B样条配准的速度与精度。然后使用局部区域细化层次B样条方法将TPS生成的较稀疏的形变网格作为初始网格,结合有限记忆优化算法(L-BFGS)对控制网格做进一步地处理,此过程只对形变较大的局部区域进行细化,以实现与参考图像的快速精确配准。实验结果表明,该方法较层次B样条方法有效地提高了配准的速度和精度。     

Algorithm for registration of full Scanning Laser Ophthalmoscope video sequences

Fluorescein angiography is an established technique for examining the functional integrity of the retinal microcirculation for early detection of changes due to retinopathy. This paper describes a new method for the registration of large Scanning Laser Ophthalmoscope sequences (SLO), where the patient has been injected with a fluorescent dye. This allows the measurement of parameters such as the arteriovenous passage time. Due to the long time needed to acquire these sequences, there will inevitably be eye movement, which must be corrected prior to the application of quantitative analysis. The algorithm described here combines mutual information-based registration and landmark-based registration. The former will allow the alignment of the darkest frames of the sequence, where the dye has not still arrived to the retina, because of its ability to work with images without a preprocessing or segmentation, while the latter uses relevant features (the vessels) extracted by means of a robust creaseness operator, to get a very fast and accurate registration. The algorithm only detects rigid transformations but proves to be robust against the slight alterations derived from the eye location perspective during acquisition. Results were validated by expert clinicians.     

基于Powell算法与改进遗传算法的医学图像配准方法

针对基于互信息图像配准的局部极值问题,提出一种基于Powell算法与改进遗传算法结合的医学图像配准方法。该方法对标准遗传算法存在的收敛速度慢、易早熟、有可能导致误配的缺陷,提出了相应的改进策略; 采用Logistic混沌映射生成迭代过程中的个体; 运用基于小波变换的多分辨率分析策略,采用混合优化算法在图像的最低分辨率层进行全局优化,以全局最优值,结合Powell算法完成医学图像配准。实验结果表明,所提方法可有效避免优化算子陷入局部极值,并提高了配准速度; 相对于纯Powell方法和未改进的遗传算法,配准的精确度和性能更好。     

Scale-Free Registrations in 3D: 7 Degrees of Freedom with Fourier Mellin SOFT Transforms

Fourier Mellin SOFT (FMS) as a novel method for global registration of 3D data is presented. It determines the seven degrees of freedom (7-DoF) transformation, i.e., the 6-DoF rigid motion parameters plus 1-DoF scale, between two scans, i.e., two noisy, only partially overlapping views on objects or scenes. It is based on a sequence of the 3D Fourier transform, the Mellin transform and the SO(3) Fourier transform. This combination represents a non-trivial complete 3D extension of the well known Fourier-Mellin registration for 2D images. It is accordingly based on decoupling rotation and scale from translation. First, rotation—which is the main challenge for the extension to 3D data - is tackled with a SO(3) Fourier Transform (SOFT) based on Spherical Harmonics. In a second step, scale is determined via a 3D Mellin transform. Finally, translation is calculated by Phase-Matching. Experiments are presented with simulated data sets for ground truth comparisons and with real world data including object recognition and localization in Magnetic Resonance Tomography (MRT) data, registration of 2.5D RGBD scans from a Microsoft Kinect with a scale-free 3D model generated by Multi-View Vision, and 3D mapping by registration of a sequence of consecutive scans from a low-cost actuated Laser Range Finder. The results show that the method is fast and that it can robustly handle partial overlap, interfering structures, and noise. It is also shown that the method is a very interesting option for 6-DoF registration, i.e., when scale is known.     

Multimodality image registration by maximization of quantitative–qualitative measure of mutual information

This paper presents a novel image similarity measure, referred to as quantitative–qualitative measure of mutual information (Q-MI), for multimodality image registration. Conventional information measures, e.g., Shannon's entropy and mutual information (MI), reflect quantitative aspects of information because they only consider probabilities of events. In fact, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence. Thus, it is important to consider both quantitative (i.e., probability) and qualitative (i.e., utility) measures of information in order to fully capture the characteristics of events. Accordingly, in multimodality image registration, Q-MI should be used to integrate the information obtained from both the image intensity distributions and the utilities of voxels in the images. Different voxels can have different utilities, for example, in brain images, two voxels can have the same intensity value, but their utilities can be different, e.g., a white matter (WM) voxel near the cortex can have higher utility than a WM voxel inside a large uniform WM region. In Q-MI, the utility of each voxel in an image can be determined according to the regional saliency value calculated from the scale-space map of this image. Since the voxels with higher utility values (or saliency values) contribute more in measuring Q-MI of the two images, the Q-MI-based registration method is much more robust, compared to conventional MI-based registration methods. Also, the Q-MI-based registration method can provide a smoother registration function with a relatively larger capture range. In this paper, the proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images.     

基于线特征和控制点的可见光和SAR图像配准

以具有典型人造目标的可见光和SAR (Synthetic aperture radar)图像为研究对象,提出一种自适应多尺度快速Beamlet变换方法提取人造目标在可见光和SAR图像的共有特征---线特征, 并基于线特征构造控制点,设计了一种基于控制点特征的匹配度函数,采用基于特征一致的粗配准和基于控制点的精确配准方法,对待配准图像实现由粗到精的自动配准. 实验表明,在可见光和SAR图像存在较大灰度差异、旋转和平移的情况下,该算法仍然能够精确配准图像.     

Nonlinear analysis of an indirectly controlled limit cycle walker

Towards controlling the frequency of limit cycle locomotion as well as adapting to rough terrain and performing specific tasks, a novel and indirect method has been recently introduced using an active wobbling mass attached to limit cycle walkers. One of the strongest advantages of the method is the easiness of its implementation, prompting its applicability to a wide variety of locomotion systems. To deeply understand the nonlinear dynamics for further enhancement of the methodology, we use a combined rimless wheel with an active wobbling mass as an example to perform nonlinear analysis in this paper. First, we introduce the simplified equation of motion and the gait frequency control method. Second, we obtain Arnold tongue, which represents region of entrained locomotion. In regions where the locomotion is not entrained, we explore chaotic and quasi-periodic gaits. To characterize bistability of two different locomotions that underlie hysteresis phenomena, basins of attraction for the two locomotions were computed. The present nonlinear analysis may help understanding the detailed mechanism of indirectly controlled limit cycle walkers.     

与文本无关的说话人自适应确认方法

该文提出一种与文本无关的自适应说话人确认方法.此自适应方法基于作者所提出的用全局说话人模型标准化似然得分值进行说话人确认的方法,以解决此方法应用于实际系统时存在的训练时间较长的问题,从而缩短新用户注册系统的等待时间,使新用户能够在较短的时间内开始系统的使用.实验结果充分说明了此方法的有效性;当系统有30个用户时,新用户的注册速度加快了12倍.     

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Cardiovascular disease is the leading cause of death worldwide and for this reason computer-based diagnosis of cardiac diseases is a very important task. In this article, a method for segmentation of aortic outflow velocity profiles from cardiac Doppler ultrasound images is presented. The proposed method is based on the statistical image atlas derived from ultrasound images of healthy volunteers. The ultrasound image segmentation is done by registration of the input image to the atlas, followed by a propagation of the segmentation result from the atlas onto the input image. In the registration process, the normalized mutual information is used as an image similarity measure, while optimization is performed using a multiresolution gradient ascent method. The registration method is evaluated using an in-silico phantom, real data from 30 volunteers, and an inverse consistency test. The segmentation method is evaluated using 59 images from healthy volunteers and 89 images from patients, and using cardiac parameters extracted from the segmented image. Experimental validation is conducted using a set of healthy volunteers and patients and has shown excellent results. Cardiac parameter segmentation evaluation showed that the variability of the automated segmentation relative to the manual is comparable to the intra-observer variability. The proposed method is useful for computer aided diagnosis and extraction of cardiac parameters.     

Optimization of dynamic shear force measurement for fiber wobbling method

Fiber wobbling method can be used to study nanotribological properties of a head disk interface. It can measure 1 nN shear force at a high shear rate. In this study, an optical system for the shear force measurement was optimized considering the spherical aberration of a sphere lens and the saturation of the position sensitive detector, and the minimum shear force detection limit of sub-nano-newton has been achieved.     

大形变微分同胚图像配准快速算法

本文提出一种研究大形变图像配准算法. 大形变使得图像信息和拓扑结构有较大的改变, 目前该方面的研究仍然是一个难点. 基于严密数学理论的微分同胚Demons算法是图像配准的著名算法, 为解决大形变配准问题提供了重要基础. 基于对微分同胚Demons算法的研究结合流形学习的思想提出一种大形变图像配准的新算法(MRL算法). 新算法通过挖掘图像的局部和全局流形信息改进微分同胚Demons 速度场的更新, 更好地保持图像的拓扑结构. 对比实验结果表明, 本文所提出的算法能够快速高精度地实现大形变图像的配准.