基于Fourier-Mellin变换和Keren算法的改进运动估计算法

针对超分辨率图像重建中图像运动估计精度要求高,速度要求快的问题,对传统的基于Fourier-Mellin变换和Keren算法的运动估计方法做出以下改进:首先提取参考图像和待估计图像的边缘,从而避免了Fourier-Mellin变换的不足(对细节不明显的图像运动估计精度极差);由于只是用Fourier-Mellin变换进行粗估计,对角度估计精度要求不高,只需小于1°,因此在进行对数极坐标变换时,可以减少角度坐标和对数坐标的采样点数,大幅缩小了矩阵大小,提高了运动估计速度;由于先用Fourier-Mellin对待估计图像进行粗估计,Keren算法可以避开复杂的金字塔计算而只需一层估计,减少了运动估计时间。在VC++中的仿真实验表明,该方法有效地结合了Fourier-Mellin变换和Keren算法的优点,同时又提高了运动估计速度。经测试,用未改进的算法对328×500像素大小的两幅图像进行运算估计需要3.53s,而用改进的算法则只需要1.15s,大大提高了运动估计速度。     

基于Fourier-Mellin变换和相干系数法的重复轨道干涉SAR图像配准新方法

相对于单轨双天线InSAR系统,重复轨道InSAR系统由于轨道不能完全平行,导致InSAR图像对间存在夹角,使其配准更为困难。该文针对非平行重复轨道的InSAR问题,提出了一种结合Fourier-Mellin变换和相干系数法的InSAR图像配准算法,对存在夹角的SAR复图像对进行精确配准。该方法用于粗配准时,通过引入非线性图像幅度变换预处理,克服了Fourier-Mellin变换无法直接处理SAR图像对幅度分布动态范围大且不均匀的问题,而用于精配准时,通过引入相干系数法,精确估计了复图像对之间存在的微小角度。以SIR-C/X重复轨道的X波段SAR数据为例,详细比较了传统相干系数配准算法和该文算法在配准精度和计算时间上的差异,从而验证了算法的有效性。     

Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery

Intraoperative freehand three-dimensional (3-D) ultrasound (3D-US) has been proposed as a noninvasive method for registering bones to a preoperative computed tomography image or computer-generated bone model during computer-aided orthopedic surgery (CAOS). In this technique, an US probe is tracked by a 3-D position sensor and acts as a percutaneous device for localizing the bone surface. However, variations in the acoustic properties of soft tissue, such as the average speed of sound, can introduce significant errors in the bone depth estimated from US images, which limits registration accuracy. We describe a new self-calibrating approach to US-based bone registration that addresses this problem, and demonstrate its application within a standard registration scheme. Using realistic US image data acquired from 6 femurs and 3 pelves of intact human cadavers, and accurate Gold Standard registration transformations calculated using bone-implanted fiducial markers, we show that self-calibrating registration is significantly more accurate than a standard method, yielding an average root mean squared target registration error of 1.6 mm. We conclude that self-calibrating registration results in significant improvements in registration accuracy for CAOS applications over conventional approaches where calibration parameters of the 3D-US system remain fixed to values determined using a preoperative phantom-based calibration.     

对数极坐标变换域下互信息图像配准方法

当待配准中的图像中存在较大的尺度变换时,传统的图像互信息配准方法对图像尺度变换参数很敏感,易于在优化搜索时陷入局部极值点,配准效果不好。文中提出基于对数极坐标（log—polar）域的图像配准方法,它较好地分离了图像缩放参数和图像旋转参数的相互影响。仿真结果表明,该方法在大尺度图像配准场合应用时比通常的互信息方法有更高的精度和鲁棒性。     

Identification of image and blur parameters in frequency domainusing the EM algorithm

We extend a method presented previously, which considers the problem of the semicausal autoregressive (AR) parameter identification for images degraded by observation noise only. We propose a new approach to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the observation noise power and the PSF of the degradation. We decompose the image into 1-D independent complex scalar subsystems resulting from the vector state-space model by using the unitary discrete Fourier transform (DFT). Then, by applying the expectation-maximization (EM) algorithm to each subsystem, we identify the AR model and blur parameters of the transformed image. The AR parameters of the original image are then identified by using the least squares (LS) method. The restored image is obtained as a byproduct of the EM algorithm.     

基于Radon和解析Fourier-Mellin变换的篡改图像盲检测算法

复制粘贴（Copy-Move）是一种极为常见的图像篡改方式。为了快速有效地检测图像经过旋转、缩放等操作后的篡改图像,本文提出了一种基于Radon和解析Fourier-Mellin变换的篡改图像盲检测方法。文章首先对图像进行分块,之后将图像块进行Radon和解析Fourier-Mellin变换,并提取计算变换结果后的矩特征值,最后计算矩特征值的相关性。本文算法不需要对灰度图像进行二值化与归一化处理,而是直接从图形的Radon变换与Fourier-Mellin变换的结果中提取不变特征,理论分析与实验结果表明,本文提出算法的检测结果优于基于正交矩的检测方法,而且对均值为0的白噪声的鲁棒性显著高于基于正交矩的检测方法。      

基于图像内在特征的图像自动拼接方法

针对传统图像拼接方法需要人工干预的不足,提出了基于图像内在特征的图像自动拼接方法.本方法应用由粗到精的技术实现图像之间像素精度的拼接.图像粗配准阶段采用仿射变换模型,利用Fourier-Mellin变换的频域分析技术同图像多分辨率技术相结合的方法求取仿射变换模型下的粗配准参数.图像的精配准阶段采用投影变换模型,将粗配准参数作为图像投影变换模型下的初始值,相邻图像中重叠区域的灰度差平方和作为误差指标函数,利用Levenberg-Marquardt方法进行图像投影模型参数估计的优化,得到全局意义条件下的精确变换参数.最后,将此方法应用于实际拍摄的可见光和红外图像序列进行试验分析,验证了本方法的有效性.     

三维集成成像中无串扰显示系统的设计与实现

通过对集成成像原理和串扰现象原理的分析,基于显示系统的串扰与观察距离的关系和人眼视觉特性,提出了显示系统的无串扰设计方法。设计实例及实验结果表明,本文的三维集成成像显示系统的无串扰设计方法不仅使在预期的观察位置观看所得的三维集成像不再受串扰现象影响,而且满足人眼视觉分辨特性要求。     

星空背景中目标识别算法研究

为了实时捕获混杂在天基背景中的运动目标,采用基于Fourier—Mellin图像配准与边缘提取相结合的差分算法。先采用Fourier—Mellin算法和相位相关算法求取图像的旋转角度及缩放和平移参数,得出配准点,再对连续采集的两帧图像配准、差分、二值化,将差分图像与后一帧边缘提取的图像进行逻辑乘,得到目标。仿真结果表明,该算法虚警率低,适合微型航天器多目标捕获的要求。     

Motion estimation of skeletonized angiographic images using elastic registration

An approach for estimating the motion of arteries in digital angiographic image sequences is proposed. Binary skeleton images are registered using an elastic registration algorithm in order to estimate the motion of the corresponding arteries. This algorithm operates recursively on the skeleton images by considering an autoregressive (AR) model of the deformation in conjunction with a dynamic programming (DP) algorithm. The AR model is used at the pixel level and provides a suitable cost function to DP through the innovation process. In addition, a moving average (MA) model for the motion of the entire skeleton is used in combination with the local AR model for improved registration results. The performance of this motion estimation method is demonstrated on simulated and real digital angiographic image sequences. It is shown that motion estimation using elastic registration of skeletons is very successful especially with low contrast and noisy angiographic images.     

Causal and semicausal AR image model identification using the EMalgorithm

The method presented by T. Katayama and T. Hirai (1990), who considered the problem of semicausal autoregressive (AR) parameter identification for images degraded by observation noise, is extended. In particular, an approach to identifying both the causal and semicausal AR parameters without a priori knowledge of the observation noise power is proposed. The image is decomposed into 1-D independent complex scalar subsystems resulting from the vector state-space model, using the unitary discrete Fourier transform (DFT). Then the expectation-maximization algorithm is applied to each subsystem to identify the AR parameters of the transformed image. The AR parameters of the original image are then identified using the least-square method. The restored image is obtained as a byproduct of the EM algorithm.     

Fluoroscopic bone fragment tracking for surgical navigation in femur fracture reduction by incorporating optical tracking of hip joint rotation center

A new method for fluoroscopic tracking of a proximal bone fragment in femoral fracture reduction is presented. The proposed method combines 2-D and 3-D image registration from single-view fluoroscopy with tracking of the head center position of the proximal femoral fragment to improve the accuracy of fluoroscopic registration without the need for repeated manual adjustment of the C-arm as required in stereo-view registrations. Kinematic knowledge of the hip joint, which has a positional correspondence with the femoral head center and the pelvis acetabular center, allows the position of the femoral fragment to be determined from pelvis tracking. The stability of the proposed method with respect to fluoroscopic image noise and the desired continuity of the fracture reduction operation is demonstrated, and the accuracy of tracking is shown to be superior to that achievable by single-view image registration, particularly in depth translation.     

A bound on mutual information for image registration

An upper bound is derived for the mutual information between a fixed image and a deformable template containing a fixed number of gray-levels. The bound can be calculated by maximizing the entropy of the template under the constraint that the conditional entropy of the template, given the fixed image, be zero. This bound provides useful insight into the properties of mutual information as a similarity metric for deformable image registration. Specifically, it indicates that maximizing mutual information may not necessarily produce an optimal solution when the deformable transform is too flexible.     

3-D radar imaging using range migration techniques

An imaging system with three-dimensional (3-D) capability can be implemented by using a stepped frequency radar which synthesizes a two-dimensional (2-D) planar aperture. A 3-D image can be formed by coherently integrating the backscatter data over the measured frequency band and the two spatial coordinates of the 2-D synthetic aperture. This paper presents a near-field 3-D synthetic aperture radar (SAR) imaging algorithm. This algorithm is an extension of the 2-D range migration algorithm (RMA). The presented formulation is justified by using the method of the stationary phase (MSP). Implementation aspects including the sampling criteria, resolutions, and computational complexity are assessed. The high computational efficiency and accurate image reconstruction of the algorithm are demonstrated both with numerical simulations and measurements using an outdoor linear SAR system     

Markerless real-time 3-D target region tracking by motion backprojection from projection images

Accurate and fast localization of a predefined target region inside the patient is an important component of many image-guided therapy procedures. This problem is commonly solved by registration of intraoperative 2-D projection images to 3-D preoperative images. If the patient is not fixed during the intervention, the 2-D image acquisition is repeated several times during the procedure, and the registration problem can be cast instead as a 3-D tracking problem. To solve the 3-D problem, we propose in this paper to apply 2-D region tracking to first recover the components of the transformation that are in-plane to the projections. The 2-D motion estimates of all projections are backprojected into 3-D space, where they are then combined into a consistent estimate of the 3-D motion. We compare this method to intensity-based 2-D to 3-D registration and a combination of 2-D motion backprojection followed by a 2-D to 3-D registration stage. Using clinical data with a fiducial marker-based gold-standard transformation, we show that our method is capable of accurately tracking vertebral targets in 3-D from 2-D motion measured in X-ray projection images. Using a standard tracking algorithm (hyperplane tracking), tracking is achieved at video frame rates but fails relatively often (32% of all frames tracked with target registration error (TRE) better than 1.2 mm, 82% of all frames tracked with TRE better than 2.4 mm). With intensity-based 2-D to 2-D image registration using normalized mutual information (NMI) and pattern intensity (PI), accuracy and robustness are substantially improved. NMI tracked 82% of all frames in our data with TRE better than 1.2 mm and 96% of all frames with TRE better than 2.4 mm. This comes at the cost of a reduced frame rate, 1.7 s average processing time per frame and projection device. Results using PI were slightly more accurate, but required on average 5.4 s time per frame. These results are still substantially faster than 2-D to 3-D registration. We conclude that motion backprojection from 2-D motion tracking is an accurate and efficient method for tracking 3-D target motion, but tracking 2-D motion accurately and robustly remains a challenge.     

Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images

One of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.      

Surgical navigation by autostereoscopic image overlay of integral videography

This paper describes an autostereoscopic image overlay technique that is integrated into a surgical navigation system to superimpose a real three-dimensional (3-D) image onto the patient via a half-silvered mirror. The images are created by employing a modified version of integral videography (IV), which is an animated extension of integral photography. IV records and reproduces 3-D images using a microconvex lens array and flat display; it can display geometrically accurate 3-D autostereoscopic images and reproduce motion parallax without the need for special devices. The use of semitransparent display devices makes it appear that the 3-D image is inside the patient's body. This is the first report of applying an autostereoscopic display with an image overlay system in surgical navigation. Experiments demonstrated that the fast IV rendering technique and patient-image registration method produce an average registration accuracy of 1.13 mm. Experiments using a target in phantom agar showed that the system can guide a needle toward a target with an average error of 2.6 mm. Improvement in the quality of the IV display will make this system practical and its use will increase surgical accuracy and reduce invasiveness.     

Automatic correction of registration errors in surgical navigation systems

Surgical navigation systems are used widely among all fields of modern medicine, including, but not limited to ENT- and maxillofacial surgery. As a fundamental prerequisite for image-guided surgery, intraoperative registration, which maps image to patient coordinates, has been subject to many studies and developments. While registration methods have evolved from invasive procedures like fixed stereotactic frames and implanted fiducial markers toward surface-based registration and noninvasive markers fixed to the patient's skin, even the most sophisticated registration techniques produce an imperfect result. Due to errors introduced during the registration process, the projection of navigated instruments into image data deviates up to several millimeter from the actual position, depending on the applied registration method and the distance between the instrument and the fiducial markers. We propose a method that allows to automatically and continually improve registration accuracy during intraoperative navigation after the actual registration process has been completed. The projections of navigated instruments into image data are inspected and validated by the navigation software. Errors in image-to-patient registration are identified by calculating intersections between the virtual instruments' axes and surfaces of hard bone tissue extracted from the patient's image data. The information gained from the identification of such registration errors is then used to improve registration accuracy by adding an additional pair of registration points at every location where an error has been detected. The proposed method was integrated into a surgical navigation system based on paired points registration with anatomical landmarks. Experiments were conducted, where registrations with deliberately misplaced point pairs were corrected with automatic error correction. Results showed an improvement in registration quality in all cases.     

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。     

Fourier-Mellin矩在图像识别中的应用

为了验证Fourier—Me｜lin矩图像识别中的识别能力,本文研究了其在两种坐标下的计算和重建效果、抗噪性试验。在笛卡尔坐标下,图像重建直接计算,不必转换为极坐标,避免极坐标在了转换时产生的几何和计算误差,试验表明：笛卡尔坐标下,Fourier—Mellin矩的重建比极坐标下更精确,并且OFFM矩对噪声有很好的鲁棒性...