结合Broyden族算法和微分同胚变换的图像自动配准方法

本文采用微分同胚变换预处理图像,得到初始化形变场,提高对形变图像的配准精度;采用Broyden族算法优化能量函数,自动确定迭代次数,提高优化效率;基于Demons算法思想引入图像梯度灰度场相似量构造能量函数,提高灰度信息少的图像配准精度。实验证明,本文算法配准精度优于改进的Demons算法,尤其在配准大形变图像时,本文算法配准精度高的优势更加明显。     

基于平流矢量场聚类的图像复杂空间映射配准

复杂形变下的图像配准在用几何约束模型逼近时存在固有误差,造成配准细节失真.提出一种根据计算流体动力学获取图像像素空间复杂映射的方法,用于局部形变下的图像配准、拼接等任务.利用二维空间网格上的特征点无监督聚类得到图像平流输运矢量场,并将鲁棒估计器嵌入最小二乘迭代以缓解特征点匹配困难,最后依据散度消除映射流在奇异点处的输出...     

基于改进光流场模型的脑部多模医学图像配准

 基于光流场模型的配准其常亮假设的光流场约束要求待配准的源图像和目标图像具有一致的灰度,因而只适用于单模态图像之间的配准,为此使用基于排序的精确直方图规定化对脑部MR-PD图像进行模态变换,完成与MR-T2图像之间的灰度映射.由于此配准方法主要用来寻找时间序列图像中的细小形变,当待配准的两图像差异较大时就不能取得满意的配准效果,为此使用能反映图像结构的标记点构造附加的外力对光流场模型加以改进,以获得更理想的配准参数.实验证明,本文方法能够实现具有较大差异的脑部MR多模序列图像之间的准确配准.     

基于结构保持的MR图像运动伪影快速抑制方法

目前核磁共振图像运动伪影的校正方法普遍是基于K空间数据的方法,本文提出一种直接对核磁共振图像进行伪影校正的后处理方法.基于非局部均值总变差去噪的思想设计构造了结构保持的运动伪影校正模型,该模型由非局部均值正则项和块相似保真项构成,正则项可以有效去除运动伪影和噪声的同时保持图像的结构;将各向异性结构张量作为块相似保真项中的权函数,实现在不同区域有不同的扩散方式,在去除图像运动伪影的同时保留图像的细节信息.模型的数值求解采用分裂Bregman方法实现.本文提出的方法充分考虑了图像的几何结构特性,实验结果表明,该方法能有效去除运动伪影并保留有价值的图像细节信息,同时提高了运算速度.     

一种新的无人机航拍序列图像快速拼接方法

 无人驾驶飞机航拍序列图像的邻近帧间含有大量的相交区域,这些相交区域是进行基于特征的图像拼接的基础,但是也成为了降低拼接效率的冗余信息.因此,本文提出一种既稳定又具有较小时间开销的无人机航拍序列图像的自动拼接方法.利用图像拼接过程中查找到的匹配特征点,自适应提取出适于航拍序列图像快速拼接的关键帧;在分析了序列图像的拼接时的相交区域的运动模型的基础上,建立了无人机航拍序列图像配准过程中的特征搜索区域预测的卡尔曼滤波器,减少了特征搜索和配准的时间;然后给出了详细无人机航拍序列图像的快速拼接方法,实验结果显示,本文的方法具有较好的拼接效果和拼接效率.     

结合相位和ＳＩＦＴ特征相关的图像自动配准算法

提出一种鲁棒性较好的图像配准算法解决乱序输入图像序列自动拼接问题。首先为输入图像建立多分辨率金字塔,在较低分辨率图像层上进行相位相关区域搜索;接着对得到的相关区域进行SIFT特征相关验证,得到图像序列的配准关系和透视变换矩阵的初步估计;然后结合估计参数对原始图像的SIFT特征再次进行相关匹配,最终实现图像序列变换参数的精确求解。该算法结合相位相关和SIFT特征相关两种配准算法的优点并应用由粗到精的匹配策略提高了图像配准的速度和稳定性。     

基于图像梯度场序列的双向GDIM光流计算方法

提出了一种基于图像梯度矢量场的双向广义动态图像模型(GFBD-GDIM)光流计算方法.本文方法:在图像梯度场上进行光流估计以减弱光照变化带来的影响;将一个大的运动矢量分解为两个不同方向的子矢量进行估计,有助于减小估计误差,提高计算精度;采用广义动态图像模型(GDIM)对图像梯度场的变化进行建模,可使模型适用于更加一般的场合.图像序列实验表明,本文方法可以获得更加精确和鲁棒的运动矢量估计.     

基于标准剂量CT图像非局部权值先验的低剂量图像恢复

本文提出一种基于标准剂量CT图像非局部权值先验的低剂量图像恢复方法,该方法首先将先前标准剂量图像与低剂量CT图像配准,并对低剂量CT图像进行预处理以抑制部分噪声,随后利用非局部均值的思想计算配准后的先前标准剂量CT图像的权值矩阵,基于该权值矩阵对预处理后的低剂量CT图像进行加权均值滤波.仿真实验和临床脑灌注数据实验表明,本文方法在消除低剂量CT图像噪声和伪影的同时,还可提升图像分辨率,对临床脑灌注CT扫描的疾病诊断中尤为有效.     

基于场景复杂度与不变特征的航拍视频实时配准算法

实时、鲁棒的图像配准是航拍视频电子稳像、全景图拼接和地面运动目标自动检测与跟踪的前提和关键技术.本文以航拍视频序列为处理对象,提出了一种新的基于场景复杂度与不变特征的实时配准算法,其主要特点包括：(1)在对航拍视频配准难点进行详细分析的基础上,有针对性的提出基于积分图的快速图像尺度空间构建、依据场景复杂度的检测特征点数量在线精确控制、基于描述子误差分布统计特性级的联分类器构造等新方法,使得算法配准性能不随场景的复杂度发生改变,能够在各种地貌条件下实时、稳定的进行图像配准;(2)将多尺度Harris角点和SIFT描述子相结合,并通过对帧间变换模型参数进行鲁棒估计,保证了算法具有良好的旋转、尺度、亮度不变性和配准精度.实验结果表明,算法可在场景变化、图像大幅度平移、尺度缩放和任意角度旋转等复杂条件下实时、精确的进行图像配准,对分辨率为320×240的航拍序列的平均处理速度达到20.7帧/秒.     

PROPELLER磁共振成像数据重建中的仿射运动校正新算法

 PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction)磁共振成像方法对刚性运动伪影的消除效果非常显著,已经在头部磁共振成像中获得了成功应用。但是刚性运动一般仅存在于头部成像中,人体其它部位成像往往伴随着不同程度的软组织拉伸变形。对于这种软组织变形必须基于非刚性运动模型才能准确地进行描述并加以校正。本文将PROPELLER采样中的每个k-空间条经过傅立叶逆变换重建得到临时图像,通过基于仿射运动模型的图像配准算法获得非刚性运动信息,然后根据仿射变换的频域性质,对PROPELLER采样中的每个k-空间条进行校正,最后经网格化重建得到最终图像。仿真实验与真实数据实验表明,相对于现有的PROPELLER重建算法,本文所提算法对于刚性运动与仿射运动造成的伪影均具有很好的校正效果。     

Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration

Tagged magnetic resonance imaging (MRI) is unique in its ability to noninvasively image the motion and deformation of the heart in vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper, we present a novel and fully automated technique based on nonrigid image registration using multilevel free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. The novel aspect of our technique is its integrated nature for tag localization and deformation field reconstruction using image registration and voxel based similarity measures. To extract the motion field within the myocardium during systole we register a sequence of images taken during systole to a set of reference images taken at end-diastole, maximizing the normalized mutual information between the images. We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We also present validation results from data acquired from twelve volunteers.     

Semiautomatic 3-D image registration as applied to interventional MRI liver cancer treatment

We evaluated semiautomatic, voxel-based registration methods for a new application, the assessment and optimization of interventional magnetic resonance imaging (I-MRI) guided thermal ablation of liver cancer. The abdominal images acquired on a low-field-strength, open I-MRI system contain noise, motion artifacts, and tissue deformation. Dissimilar images can be obtained as a result of different MRI acquisition techniques and/or changes induced by treatments. These features challenge a registration algorithm. We evaluated one manual and four automated methods on clinical images acquired before treatment, immediately following treatment, and during several follow-up studies. Images were T2-weighted, T1-weighted Gd-DTPA enhanced, T1-weighted, and short-inversion-time inversion recovery (STIR). Registration accuracy was estimated from distances between anatomical landmarks. Mutual information gave better results than entropy, correlation, and variance of gray-scale ratio. Preprocessing steps such as masking and an initialization method that used two-dimensional (2-D) registration to obtain initial transformation estimates were crucial. With proper preprocessing, automatic registration was successful with all image pairs having reasonable image quality. A registration accuracy of approximately equal to 3 mm was achieved with both manual and mutual information methods. Despite motion and deformation in the liver, mutual information registration is sufficiently accurate and robust for useful applications in I-MRI thermal ablation therapy.     

Correlation estimation from compressed images

This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where the images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the images are correlated through the displacement of visual objects due to motion or viewpoint change and the correlation is effectively represented by optical flow or motion field models. The correlation is estimated in the compressed domain by jointly processing the linear measurements. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship we then describe the dependencies between images in the compressed domain. We further cast a regularized optimization problem where the correlation is estimated in order to satisfy both data consistency and motion smoothness objectives with a Graph Cut algorithm. We analyze in detail the correlation estimation performance and quantify the penalty due to image compression. Extensive experiments in stereo and video imaging applications show that our novel solution stays competitive with methods that implement complex image reconstruction steps prior to correlation estimation. We finally use the estimated correlation in a novel joint image reconstruction scheme that is based on an optimization problem with sparsity priors on the reconstructed images. Additional experiments show that our correlation estimation algorithm leads to an effective reconstruction of pairs of images in distributed image coding schemes that outperform independent reconstruction algorithms by 2–4 dB.     

Constrained registration for motion compensation in atrial fibrillation ablation procedures

Fluoroscopic overlay images rendered from preoperative volumetric data can provide additional anatomical details to guide physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are often compromised by cardiac and respiratory motion, motion compensation methods are needed to keep the overlay images in sync with the fluoroscopic images. So far, these approaches have either required simultaneous biplane imaging for 3-D motion compensation, or in case of monoplane X-ray imaging, provided only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose an approach that facilitates a full 3-D motion compensation even if only monoplane X-ray images are available. To this end, we use a training phase that employs a biplane sequence to establish a patient specific motion model. Afterwards, a constrained model-based 2-D/3-D registration method is used to track a circumferential mapping catheter. This device is commonly used for AFib catheter ablation procedures. Based on the experiments on real patient data, we found that our constrained monoplane 2-D/3-D registration outperformed the unconstrained counterpart and yielded an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 1.6 mm. The unconstrained 2-D/3-D registration technique yielded a similar 2-D performance, but the 3-D tracking error increased to 3.2 mm mostly due to wrongly estimated 3-D motion components in X-ray view direction. Compared to the conventional 2-D monoplane method, the proposed method provides a more seamless workflow by removing the need for catheter model re-initialization otherwise required when the C-arm view orientation changes. In addition, the proposed method can be straightforwardly combined with the previously introduced biplane motion compensation technique to obtain a good trade-off between accuracy and radiation dose reduction.     

A MAP approach for joint motion estimation, segmentation, and super resolution.

Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm.     

Ultrasound elastography based on multiscale estimations of regularized displacement fields

Elasticity imaging is based on the measurements of local tissue deformation. The approach to ultrasound elasticity imaging presented in this paper relies on the estimation of dense displacement fields by a coarse-to-fine minimization of an energy function that combines constraints of conservation of echo amplitude and displacement field continuity. The multiscale optimization scheme presents several characteristics aimed at improving and accelerating the convergence of the minimization process. This includes the nonregularized initialization at the coarsest resolution and the use of adaptive configuration spaces. Parameters of the energy model and optimization were adjusted using data obtained from a tissue-like phantom material. Elasticity images from normal in vivo breast tissue were subsequently obtained with these parameters. Introducing a smoothness constraint into motion field estimation helped solve ambiguities due to incoherent motion, leading to elastograms less degraded by decorrelation noise than the ones obtained from correlation-based techniques.     

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness. We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method.     

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.     

基于图像强度最优的SAR高精度运动补偿方法

由于载体平台的不稳定性和测量传感器的精度限制,运动误差成为了提高合成孔径雷达(SAR)成像质量的一个瓶颈。基于图像锐度最优的自聚焦后向投影算法通过估计相位误差进行运动补偿,具有较高精度,但这种方法假设场景中所有像素点相位误差相同,即没有考虑运动误差的空变性,导致大部分像素点仍存在残留误差,造成成像质量下降。针对运动误差空变性的问题,该文提出一种高精度运动补偿方法,该方法在图像强度最大准则下,采用最优化技术估计天线相位中心测量误差,随后利用该测量误差估计量校正天线相位中心并进行后向投影成像。由于估计天线相位中心等效于估计每个像素点的距离历史,因此该方法可以对每个像素点进行高精度相位补偿。点目标仿真和实测数据处理结果均验证了所提方法的有效性。      

Joint MAP registration and high-resolution image estimation using asequence of undersampled images

In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for estimating a high-resolution image, with reduced aliasing, from a sequence of undersampled frames. Several approaches to this problem have been investigated previously. However, in this paper a maximum a posteriori (MAP) framework for jointly estimating image registration parameters and the high-resolution image is presented. Several previous approaches have relied on knowing the registration parameters a priori or have utilized registration techniques not specifically designed to treat severely aliased images. In the proposed method, the registration parameters are iteratively updated along with the high-resolution image in a cyclic coordinate-descent optimization procedure. Experimental results are provided to illustrate the performance of the proposed MAP algorithm using both visible and infrared images. Quantitative error analysis is provided and several images are shown for subjective evaluation.