Rapid elastic image registration for 3-D ultrasound

A Subvolume-based algorithm for elastic Ultrasound REgistration (SURE) was developed and evaluated. Designed primarily to improve spatial resolution in three-dimensional compound imaging, the algorithm registers individual image volumes nonlinearly before combination into compound volumes. SURE works in one or two stages, optionally using MIAMI Fuse software first to determine a global affine registration before iteratively dividing the volume into subvolumes and computing local rigid registrations in the second stage. Connectivity of the entire volume is ensured by global interpolation using thin-plate splines after each iteration. The performance of SURE was quantified in 20 synthetically deformed in vivo ultrasound volumes, and in two phantom scans, one of which was distorted at acquisition by placing an aberrating layer in the sound path. The aberrating layer was designed to induce beam aberrations reported for the female breast. Synthetic deformations of 1.5-2.5 mm were reduced by over 85% when SURE was applied to register the distorted image volumes with the original ones. Registration times were below 5 min on a 500-MHz CPU for an average data set size of 13 MB. In the aberrated phantom scans, SURE reduced the average deformation between the two volumes from 1.01 to 0.30 mm. This was a statistically significant (P = 0.01) improvement over rigid and affine registration transformations, which produced reductions to 0.59 and 0.50 mm, respectively.     

改进BRISK特征的快速图像配准算法

为了实现超分辨率图像重建中高精度快速图像配准,提出一种改进BRISK特征的快速图像配准算法。原有BRISK算法在特征提取和匹配过程中,忽视了角点分布信息,其匹配策略单一,导致误匹配率高。针对该问题,首先利用BRISK算法构建连续尺度空间,在此基础上对图像进行分块,然后利用图像区域显著性自适应选择角点检测阈值,获得均匀分布的角点,最后利用快速最近邻FLANN算法结合RANSAC的方法进行二值特征快速匹配。实验结果表明:改进的BRISK算法相比原算法在保持速度的基础上达到亚像素级配准精度,并具有优越的场景适应性能。     

红外与可见光图像融合中的快速配准方法

红外与可光传感器目标跟踪识别系统中常用的两种传感器，对这两种传感器图像进行有效的融合能大大提高探测识别的准确性和可靠性。快速准确地实现图像配准是图像融合的前提，为此提出了一种基于小波变换的快速算法，有利于实时准确地实现图像配准。     

A parametric intensity-based 3D image registration method for magnetic resonance imaging

Image registration (IR) aims to map one image to another of a same scene. With rapid progress in image acquisition technologies, 3D IR becomes an important problem in magnetic resonance imaging (MRI) and other applications. In the literature, however, most IR methods are for 2D images and there are only a limited number of 3D methods available. Because 3D images have much complicated structure than their 2D counterparts, 3D IR is not just a simple generalization of the 2D IR problem. In this paper, we develop a 3D IR method that can handle cases with affine geometric transformations well. By its definition, an affine transformation maps a line to a line, and it includes rotation, translation, and scaling as special cases. In practice, most geometric transformations involved in IR problems are affine transformations. Therefore, our proposed method can find many IR applications. It is shown that this method works well in various cases, including cases when the data size of a 3D image is reduced for different reasons. This latter property makes it attractive for many 3D IR applications, since 3D images are often big in data size and it is natural to reduce their size for fast computation.     

基于SURF-DAISY算法和随机kd树的快速图像配准

提出了一种基于SURF-DAISY算法和随机kd树的快速图像配准方法,首先利用经典SURF特征检测器分别提取参考图像和待配准图像中的特征点,为了进一步提高算法速度,对SURF算法进行改进,利用DAISY描述符代替SURF原有的特征描述算法对特征点进行描述;之后通过随机kd树算法对参考图像和待配准图像的特征点进行匹配并且使用经典RANSAC(随机抽样一致性)算法剔除误匹配点对;最后用最小二乘法估算出最佳的空间几何变换参数,实现两幅图像的配准。实验结果表明:相对于标准的SURF算法,本文方法在基本保持性能不变的同时,配准过程所消耗的时间最多减少了45.6%。     

Consistent image registration

This paper presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and X-ray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear-elasticity give better registration results than using either constraint alone or none at all.     

Fast and accurate image registration using Tsallis entropy and simultaneous perturbation stochastic approximation

The Tsallis measure of mutual information is combined with the simultaneous perturbation stochastic approximation algorithm to register images. It is shown that Tsallis entropy can improve registration accuracy and speed of convergence, compared with Shannon entropy, in the calculation of mutual information. Simulation results show that the new algorithm achieves up to seven times faster convergence and four times more precise registration than using a classic form of entropy.     

基于LK和FAST的时间序列图像快速配准算法

LK光流算法是一种精确高效的特征跟踪算法,能够较大幅度提高图像配准的精度和速度。针对时间序列图像的配准问题,基于LK光流算法,通过基于图像金字塔的方式跟踪改进后的FAST特征角点,采用一种鲁棒的单应矩阵估计算法解算配准参数,提出了一种基于LK光流和改进FAST特征的实时鲁棒配准算法。通过一组时间序列图像从配准精度和配准速度两个方面对所提出算法的性能进行了验证分析,平均重投影误差为0.16,平均处理速度为30 Hz。实验结果表明,该算法能够提取稳定的FAST角点,快速准确地跟踪匹配序列图像之间的特征,较好地解决时间序列图像的实时配准问题。     

稳健的图像匹配方法

景象匹配是计算机视觉研究的一个重要方向,如何在复杂的环境下提高匹配概率与定位精度是其亟待解决的难点之一。在分析畸变校正的量化噪声为高斯噪声的基础上,提出了基于特征和时空关联的积相关图像匹配算法。采用基于噪声抑制小波边缘检测方法,提取实时图和基准图的边缘特征;基于归一化积相关实现序列实时图(3帧)与基准图的匹配,再根据位置信息实现相关峰值的数据融合得到匹配点。此方法克服了因面积增大由几何失真导致的匹配概率下降的缺点,提高了匹配概率,具有较好的匹配稳健性。     

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.     

IR image parametric up-conversion

The general considerations and principles involved with IR image up-conversion are presented and discussed. The factors that limit the resolution of the up-converted image are analyzed and the merits of three types of optical systems are discussed with regard to resolution and efficiency of up-conversion. Several characteristics of six nonlinear crystals are calculated and compared.     

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.     

Hybrid retinal image registration.

This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm.     

图像配准技术研究进展

图像配准是解决图像融合、图像镶嵌和变化检测等问题的必要前提,其应用遍及军事、遥感、医学和计算机视觉等多个领域.简要回顾了图像配准技术的发展史和研究现状,重点阐述了当前的技术热点和应用趋势,最后展望了进一步的研究方向.     

Performance bounds on image registration

Registration is a fundamental step in image processing systems where there is a need to match two or more images. Applications include motion detection, target recognition, video processing, and medical imaging. Although a vast number of publications have appeared on image registration, performance analysis is usually performed visually, and little attention has been given to statistical performance bounds. Such bounds can be useful in evaluating image registration techniques, determining parameter regions where accurate registration is possible, and choosing features to be used for the registration. In this paper, Crame/spl acute/r-Rao bounds on a wide variety of geometric deformation models, including translation, rotation, shearing, rigid, more general affine and nonlinear transformations, are derived. For some of the cases, closed-form expressions are given for the maximum-likelihood (ML) estimates, as well as their variances, as space permits. The bounds are also extended to unknown original objects. Numerical examples illustrating the analytical performance bounds are presented.     

Incremental variations of image moments for nonlinear image registration

In this paper, we use image moments to solve the problem of estimating deformation fields given a pair of images as input. We use a single family of polynomials to parameterize the deformation field and to define image moments. In this way, variations in image moments can be represented by a set of linear equations. We solve these equations iteratively for the deformation parameters between two shapes. Our approach improves existing moment-based registration methods in both robustness to noise and convergence rate. In addition, our method does not rely on solving the correspondence problem. We have extensively tested our new method on both synthetically deformed MPEG-7 shapes and real-world biomedical images.     

Coordinate-descent super-resolution and registration for parametric global motion models

We present a method for simultaneously obtaining registration and super-resolution from a sequence of low resolution images, based on a coordinate-descent approach. The novelty of the algorithm resides on the registration step, which can be applied easily to any parametric global motion model. We prove the validity of the model with synthetic and real data experiments, being of special interest the good performance achieved in the difficult case of images registered under a projective transformation.     

基于灰度的医学图像配准技术研究

医学图像配准是医学图像处理的一项重要技术,广泛应用于临床诊断和治疗、计划设计、外科手术等领域中.从基于灰度的医学图像配准角度,描述了医学图像配准的基本过程和优化策略,着重阐述了目前普遍采用的相关技术和方法的表示形式,以及这些方法的特点和应用领域,并进一步分析了在这一方面的研究中存在的问题和未来的发展方向.     

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.     

Consistent landmark and intensity-based image registration

Two new consistent image registration algorithms are presented: one is based on matching corresponding landmarks and the other is based on matching both landmark and intensity information. The consistent landmark and intensity registration algorithm produces good correspondences between images near landmark locations by matching corresponding landmarks and away from landmark locations by matching the image intensities. In contrast to similar unidirectional algorithms, these new consistent algorithms jointly estimate the forward and reverse transformation between two images while minimizing the inverse consistency error-the error between the forward (reverse) transformation and the inverse of the the reverse (forward) transformation. This reduces the ambiguous correspondence between the forward and reverse transformations associated with large inverse consistency errors. In both algorithms a thin-plate spline (TPS) model is used to regularize the estimated transformations. Two-dimensional (2-D) examples are presented that show the inverse consistency error produced by the traditional unidirectional landmark TPS algorithm can be relatively large and that this error is minimized using the consistent landmark algorithm. Results using 2-D magnetic resonance imaging data are presented that demonstrate that using landmark and intensity information together produce better correspondence between medical images than using either landmarks or intensity information alone.