Automated analysis of brachial ultrasound image sequences: early detection of cardiovascular disease via surrogates of endothelial function

Early detection of cardiovascular disease would allow timely institution of preventive measures. Arterial endothelium play a primary role in processes leading to the development of atherosclerotic plaque and cardiovascular disease in general. Determination of flow-mediated dilatation (FMD) of brachial arteries from B-mode ultrasound image sequences offers a noninvasive surrogate index of endothelial function. A highly automated method for analysis of brachial ultrasound image sequences is reported and its performance assessed. The method overcomes the variability of brachial ultrasound images across subjects by incorporating machine learning and quality control steps. The automated method outperformed conventional manual analysis by providing a decreased analysis bias, increased reproducibility, and improved measurement accuracy. Consequently, it decreases inter- and intraobserver as well interinstitution variability. The method has been employed in a number of population studies with thousands of subjects analyzed.     

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.     

An illumination invariant algorithm for subpixel accuracy image stabilization and its effect on MPEG-2 video compression

This paper offers a fast and simple near-closed-form solution for the least mean-squared-error (LMSE) estimation of the frame-to-frame global subpixel motion in an unsteady image sequence. The offered near-closed-form solution achieves unlimited subpixel accuracy by always employing a small and fixed number of computations, independent of the desired subpixel accuracy. The algorithm is designed so that it is insensitive to frame-to-frame intensity variations, which is a distinctive feature of the method. Experimental results demonstrate the superiority of the proposed method to the spatio-temporal differentiation and surface fitting algorithms under different illumination conditions. This paper furthermore discusses the effect of the proposed image stabilization algorithm on the performance of MPEG-2 video compression. We report that removal of global motion down to subpixel accuracy from an unsteady video improves MPEG-2 compression performance significantly (by at least 1 dB for all frame types), in spite of the fact that motion vectors are differentially encoded in MPEG-2. This result is supported via experimental results using the proposed subpixel registration algorithm and an analysis of the macroblock coding preferences accepted in MPEG-2.     

Correction of motion artifact in cardiac optical mapping using image registration

Cardiac motion is one of the main sources of artifacts in epifluorescence imaging experiments. It can cause significant error in electrophysiological measurements such as action potential duration. We present a novel approach that uses image registration based on maximization of mutual information to correct for in-plane cardiac motion in such experiments. The approach is relatively fast (a few seconds per frame) and is performed entirely post acquisition. The image registration approach is an alternative to traditional approaches such as mechanical restraint of the heart or addition of chemical uncouplers. Our results show that the image registration method significantly reduces motion-related artifacts in experimental data.     

Flexible mask subtraction for digital angiography

A flexible-mask algorithm that effectively provides correction for motion artifacts and gray-level variations in digital subtraction angiography (DSA) images is presented. The algorithm makes use of a flexible local registration of the mask with the live image to minimize the main distortions caused by the complex motion of the heart, namely spatial translation, rotation, and nonisotropic scaling. It also reduces the gray-level variations caused by the diffusion of the contrast medium into parts of the heart other than the arteries. It is concluded from experiments on X-ray coronary angiograms that when the background near the arteries is rugged or provides significant interference, flexible mask subtraction offers important improvements in quantitative estimates of the sizes of the arteries. In all the X-ray images used in the experiments, the precision of the method was at the 0.1-pixel level. The computation is extensive. However, some improvements can be achieved by doing the subtraction only at the coronary arteries and the immediate neighborhood instead of the whole image. The algorithm lends itself to implementation by parallel-structured hardware.     

A pyramid approach to subpixel registration based on intensity

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.     

Fast motion estimation using bidirectional gradient methods

Gradient-based motion estimation methods (GMs) are considered to be in the heart of state-of-the-art registration algorithms, being able to account for both pixel and subpixel registration and to handle various motion models (translation, rotation, affine, and projective). These methods estimate the motion between two images based on the local changes in the image intensities while assuming image smoothness. This paper offers two main contributions. The first is enhancement of the GM technique by introducing two new bidirectional formulations of the GM. These improve the convergence properties for large motions. The second is that we present an analytical convergence analysis of the GM and its properties. Experimental results demonstrate the applicability of these algorithms to real images.     

A new image registration method robust to noise

Image registration is a fundamental task in many image processing applications. In this paper, we estimate the translation, rotation, and scaling parameters between a reference image and a distorted image in order to register them. We use the ratio of means to estimate the scaling factor, the Radon transform to estimate the rotation angle, and the fast Fourier transform to estimate global spatial shifts. Experimental results show that our proposed method can estimate the transformation parameters very accurately. Furthermore, our method performs very well in noisy environments.     

Accurate image registration for MAP image super-resolution

The accuracy of image registration plays a dominant role in image super-resolution methods and in the related literature, landmark-based registration methods have gained increasing acceptance in this framework. In this work, we take advantage of a maximum a posteriori (MAP) scheme for image super-resolution in conjunction with the maximization of mutual information to improve image registration for super-resolution imaging. Local as well as global motion in the low-resolution images is considered. The overall scheme consists of two steps. At first, the low-resolution images are registered by establishing correspondences between image features. The second step is to fine-tune the registration parameters along with the high-resolution image estimation, using the maximization of mutual information criterion. Quantitative and qualitative results are reported indicating the effectiveness of the proposed scheme, which is evaluated with different image features and MAP image super-resolution computation methods.     

Nonrigid registration using free-form deformations: application tobreast MR images

In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.     

基于视频序列的数字图像拼接技术研究进展

基于视频序列的数字图像拼接是指将具有重叠区的多帧视频通过数字配准和融合获得单幅宽视场静态全景图或动态全景图.基于视频序列的数字图像拼接技术主要包括全局快速配准算法、运动目标分割算法和无缝融合算法.首先分析理想数字图像拼接系统的特性,然后介绍近年来基于视频序列的数字图像拼接技术的研究进展,最后分析其研究动向.     

基于改进蚁群算法和互信息的多光谱图像的配准

基于互信息的配准算法有精度高,自动化程度高,不需要对图像进行预处理等优点,已经被大量应用于多光谱图像配准中.但是在计算互信息时,出现了很多局部极值,这就为目标函数的寻优过程带来了很大的困难.提出了一种基于蚁群算法的配准参数寻优方法,改进蚂蚁在每层节点间的转移准则以及全局信息素的更新策略.实验证明,此方法不需要人工干预,对参数的初始值没有依赖性,配准成功率高.     

Sparse registration for three-dimensional stress echocardiography

Three-dimensional (3-D) stress echocardiography is a novel technique for diagnosing cardiac dysfunction. It involves evaluating wall motion of the left ventricle, by visually analyzing ultrasound images obtained in rest and in different stages of stress. Since the acquisitions are performed minutes apart, variabilities may exist in the visualized cross-sections. To improve anatomical correspondence between rest and stress, aligning the images is essential. We developed a new intensity-based, sparse registration method to retrieve standard anatomical views from 3-D stress images that were equivalent to the manually selected views in the rest images. Using sparse image planes, the influence of common image artifacts could be reduced. We investigated different similarity measures and different levels of sparsity. The registration was tested using data of 20 patients and quantitatively evaluated based on manually defined anatomical landmarks. Alignment was best using sparse registration with two long-axis and two short-axis views; registration errors were reduced significantly, to the range of interobserver variabilities. In 91% of the cases, the registration result was qualitatively assessed as better than or equal to the manual alignment. In conclusion, sparse registration improves the alignment of rest and stress images, with a performance similar to manual alignment. This is an important step towards objective quantification in 3-D stress echocardiography.      

Model evaluation and calibration for prospective respiratory motion correction in coronary MR angiography based on 3-D image registration

Image processing was used as a fundamental tool to derive motion information from magnetic resonance (MR) images, which was fed back into prospective respiratory motion correction during subsequent data acquisition to improve image quality in coronary MR angiography (CMRA) scans. This reduces motion artifacts in the images and, in addition, enables the usage of a broader gating window than commonly used today to increase the scan efficiency. The aim of the study reported in this paper was to find a suitable motion model to be used for respiratory motion correction in cardiac imaging and to develop a calibration procedure to adapt the motion model to the individual patient. At first, the performance of three motion models [one-dimensional translation in feet-head (FH) direction, three-dimensional (3-D) translation, and 3-D affine transformation] was tested in a small volunteer study. An elastic image registration algorithm was applied to 3-D MR images of the coronary vessels obtained at different respiratory levels. A strong intersubject variability was observed. The 3-D translation and affine transformation model were found to be superior over the conventional FH translation model used today. Furthermore, a new approach is presented, which utilizes a fast model-based image registration to extract motion information from time series of low-resolution 3-D MR images, which reflects the respiratory motion of the heart. The registration is based on a selectable global 3-D motion model (translation, rigid, or affine transformation). All 3-D MR images were registered with respect to end expiration. The resulting time series of model parameters were analyzed in combination with additionally acquired motion information from a diaphragmatic MR pencil-beam navigator to calibrate the respiratory motion model. To demonstrate the potential of a calibrated motion model for prospective motion correction in coronary imaging, the approach was tested in CMRA examinations in five volunteers.     

A nonlinear least square technique for simultaneous image registration and super-resolution.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.     

基于各向异性尺度Junction的图像拼接

图像拼接一般采用特征点匹配和全局变换,特征点仅包含位置信息,无法表达图像局部结构信息,且全局变换模型只适用于旋转拍摄和远距离拍摄情况,当图像视角变化较大时,容易产生明显的配准误差,影响拼接图像的质量。为解决这个问题,提出了一种基于各向异性尺度Junction特征的图像拼接方法。Junction不仅包含点特征信息,还具有线特征信息,表达了图像重要的局部几何结构。基于Junction特征的配准在特征点配准的基础上,利用分支线信息进一步约束和优化配准,同时结合局部单应变换模型,可以较好地容忍图像局部变形,从而提高配准精度,改善拼接效果。实验验证了本文算法的有效性。      

Combinatorial and probabilistic fusion of noisy correlation measurements for untracked freehand 3-D ultrasound

In freehand 3-D ultrasound (US), the relative positions of US images are usually measured using a position tracking device despite its cumbersome nature. The probe trajectory can instead be estimated from image data, using registration techniques to recover in-plane motion and speckle decorrelation to recover out-of-plane transformations. The relationship between speckle decorrelation and elevational separation is typically represented by a single curve, estimated from calibration data. Distances read off such a curve are corrupted by bias and uncertainty, and only provide an absolute estimate of elevational displacement. This paper presents a probabilistic model of the relationship between correlation measurements and elevational separation. This representation captures the skewed distribution of distance estimates based on high correlations and the uncertainties attached to each measurement. Multiple redundant correlation measurements can then be integrated within a maximum likelihood estimation framework. This paper also introduces a new method based on the traveling salesman problem for resolving sign ambiguities in data sets resulting from nonmonotonic probe motion and frame intersections. Experiments with real and synthetic US data show that by combining these new methods, out-of-plane US probe motion is recovered with improved accuracy over baseline methods using a deterministic model and fewer measurements.      

Bias minimizing filter design for gradient-based image registration

Gradient-based image registration techniques represent a very popular class of approaches to registering pairs or sets of images. As the name suggests, these methods rely on image gradients to perform the task of registration. Very often, little attention is paid to the filters used to estimate image gradients. In this paper, we explore the relationship between such gradient filters and their effect on overall estimation performance in registering translated images. We propose a methodology for designing filters based on image content that minimize the estimator bias inherent to gradient-based image registration. We show that minimizing such bias improves the overall estimator performance in terms of mean square error (MSE) for high signal-to-noise ratio (SNR) scenarios. Finally, we propose a technique for designing such optimal gradient filters in the context of iterative multiscale image registration and verify their further improved performance.     

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.     

Fully automated motion correction in first-pass myocardial perfusion MR image sequences

This paper presents a novel method for registration of cardiac perfusion magnetic resonance imaging (MRI). The presented method is capable of automatically registering perfusion data, using independent component analysis (ICA) to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of that ICA. This reference image is used in a two-pass registration framework. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Despite varying image quality and motion patterns in the evaluation set, validation of the method showed a reduction of the average right ventricle (LV) motion from ${1.26}pm{0.87}$ to ${0.64}pm{0.46}$ pixels. Time-intensity curves are also improved after registration with an average error reduced from ${2.65}pm {7.89}%$ to ${0.87}pm {3.88}%$ between registered data and manual gold standard. Comparison of clinically relevant parameters computed using registered data and the manual gold standard show a good agreement. Additional tests with a simulated free-breathing protocol showed robustness against considerable deviations from a standard breathing protocol. We conclude that this fully automatic ICA-based method shows an accuracy, a robustness and a computation speed adequate for use in a clinical environment.