Dual-mode registration of dynamic contrast-enhanced ultrasound combining tissue and contrast sequences

This study proposes a new method for automatic, iterative image registration in the context of dynamic contrast-enhanced ultrasound (DCE-US) imaging. By constructing a cost function of image registration using a combination of the tissue and contrast-microbubble responses, this new method, referred to as dual-mode registration, performs alignment based on both tissue and vascular structures. Data from five focal liver lesions (FLLs) were used for the evaluation. Automatic registration based on the dual-mode registration technique and tissue-mode registration obtained using the linear response image sequence alone were compared to manual alignment of the sequence by an expert. Comparison of the maximum distance between the transformations applied by the automatic registration techniques and those from expert manual registration reference showed that the dual-mode registration provided better precision than the tissue-mode registration for all cases. The reduction of maximum distance ranged from 0.25 to 9.3 mm. Dual-mode registration is also significantly better than tissue-mode registration for the five sequences with p  -values lower than 0.03$0.03$. The improved sequence alignment is also demonstrated visually by comparison of images from the sequences and the video playbacks of the motion-corrected sequences. This new registration technique better maintains a selected region of interest (ROI) within a fixed position of the image plane throughout the DCE-US sequence. This should reduce motion-related variability of the echo-power estimations and, thus, contribute to more robust perfusion quantification with DCE-US.     

基于AM测度及混合优化算法的图像配准方法

介绍了一种基于边缘特征的图像配准方法。首先利用Canny算子提取图像的边缘;然后以AM测度作为配准准则,在优化AM峰值和配准参数的时候,先用遗传算法将配准参数收敛到最优值附近,再使用Powell法进行局部搜索;最后对待配准图像进行刚体变换及双线性插值,从而实现图像配准。实验结果表明,此优化方法不仅提高了计算速度,而且提高了配准精度,配准结果达到了亚像素级水平。     

Optical superimposition of infrared thermography through video projection

This paper presents a novel infrared thermography visualization technique where a sequence of captured thermal images is optically and simultaneously superimposed onto the target object via video projection in real time. In conventional thermography visualization, observers have to frequently move their eyes from the object to a 2D screen where a thermal image is displayed. In contrast, the heat distribution of the object’s surface emerges directly onto its physical surface in the proposed method. As a result, the observer can intuitively understand the object’s heat information just by looking at it in the real space. This paper explains the methods of geometric registration and radiometric compensation of the captured thermal image, which are required before video projection. Furthermore, several projection results are shown to validate the intuitiveness and usefulness of the proposed visualization method.     

Real-time extended-field-of-view ultrasound based on a standard PC

Extended field-of-view (EFOV) can acquire a full field of vision, which can help doctors to make more objective and accurate diagnosis. Current EFOV techniques suffer from the low computation speed due to the large amount of ultrasound data to be processed. This paper describes an efficient technique to register 2D multiframe ultrasound images and produce EFOV images with significantly reduced computation time based on a standard PC. For registration of any two adjacent images, we propose to select less image blocks which are regarded as the most valid blocks based on the importance of image content. In registration of a sequence of images, with an assumption that the moving direction and speed of the probe are nearly identical during the data collection, we estimate the moving speed of the probe at the beginning of data collection and ignore redundant image data by processing a smaller number of frames according to a frame interval. The experimental results show that the computation speed of our method is increased by 7–80 times in comparison with two traditional methods, and can accurately produce EFOV images in real-time.     

车载摄像平台序列图像快速拼接方法

 针对车载摄像平台序列图像拼接中存在畸变、数据量大的问题,提出一种车载摄像平台序列图像快速拼接方法。首先对图像进行预处理来消除畸变的影响,然后对图像检测SURF特征点,定义特征点匹配率作为图像的相似性度量,根据特征点匹配率提取关键帧,再采用改进的配准策略得到全局配准模型,避免了配准误差的累积,最后采用一种最大值融合法得到序列图像的拼接图。实验结果表明：该方法具有较强的鲁棒性、更为快速。     

Accelerating image registration of MRI by GPU-based parallel computation

Automatic image registration for MRI applications generally requires many iteration loops and is, therefore, a time-consuming task. This drawback prolongs data analysis and delays the workflow of clinical routines. Recent advances in the massively parallel computation of graphic processing units (GPUs) may be a solution to this problem. This study proposes a method to accelerate registration calculations, especially for the popular statistical parametric mapping (SPM) system. This study reimplemented the image registration of SPM system to achieve an approximately 14-fold increase in speed in registering single-modality intrasubject data sets. The proposed program is fully compatible with SPM, allowing the user to simply replace the original image registration library of SPM to gain the benefit of the computation power provided by commodity graphic processors. In conclusion, the GPU computation method is a practical way to accelerate automatic image registration. This technology promises a broader scope of application in the field of image registration.     

On-line range images registration with GPGPU

This paper concerns implementation of algorithms in the two important aspects of modern 3D data processing: data registration and segmentation. Solution proposed for the first topic is based on the 3D space decomposition, while the latter on image processing and local neighbourhood search. Data processing is implemented by using NVIDIA compute unified device architecture (NIVIDIA CUDA) parallel computation. The result of the segmentation is a coloured map where different colours correspond to different objects, such as walls, floor and stairs. The research is related to the problem of collecting 3D data with a RGB-D camera mounted on a rotated head, to be used in mobile robot applications. Performance of the data registration algorithm is aimed for on-line processing. The iterative closest point (ICP) approach is chosen as a registration method. Computations are based on the parallel fast nearest neighbour search. This procedure decomposes 3D space into cubic buckets and, therefore, the time of the matching is deterministic. First technique of the data segmentation uses accele-rometers integrated with a RGB-D sensor to obtain rotation compensation and image processing method for defining pre-requisites of the known categories. The second technique uses the adapted nearest neighbour search procedure for obtaining normal vectors for each range point.     

基于CSIFT的彩色图像配准技术研究

图像配准在计算机视觉、遥感、医学诊断与治疗、环境监测等领域有广泛的研究应用.目前,多数算法是将彩色图像转化为灰度图后再配准,色彩信息的丢失可能会引起误配准.为此,提出一种基于CSIFT(Colored scale invariant feature transform)的彩色图像配准方法,求出彩色图像各个位置处的颜色不变最,以颜色不变量作为输入图像,再提取特征点并描述特征点周围的信息,通过最近邻匹配法求出图像问的匹配对,最后利用匹配的特征求取图像间的变换参数及配准后图像.实验结果表明,对彩色图像进行已知参数值变换时,该算法能得到精度高、误差小的计算结果;对变换关系末知的彩色图像,也能准确地求出图像间的映射关系;且多数情况下运行速度较SIFT(Scale invariant feature transform)快.     

Image registration guided,sparsity constrained reconstructions for dynamic MRI

It is generally a challenging task to reconstruct dynamic magnetic resonance (MR) images with high spatial and high temporal resolutions, especially with highly incomplete k-space sampling. In this work, a novel method that combines a non-rigid image registration technique with sparsity-constrained image reconstruction is introduced. Employing a multi-resolution free-form deformation technique with B-spline interpolations, the non-rigid image registration accurately models the complex deformations of the physiological dynamics, and provides artifact-suppressed high spatial-resolution predictions. Based on these prediction images, the sparsity-constrained data fidelity-enforced image reconstruction further improves the reconstruction accuracy. When compared with the k-t FOCUSS with motion estimation/motion compensation (MEMC) technique on volunteer scans, the proposed method consistently outperforms in both the spatial and the temporal accuracy with variously accelerated k-space sampling. High fidelity reconstructions for dynamic systolic phases with reduction factor of 10 and cardiac perfusion series with reduction factor of 3 are presented.     

Multispectral registration method based on stellar trajectory fitting

Image registration techniques for detecting moving targets in space require high temporal resolution and multispectral images to improve the target detection probability and reduce the false positive rate. At present, image registration accuracy is affected by the effective number of common multispectral registration control points as well as its stability. Image registration based on on-orbit stellar trajectory measurement can be used to perform on-orbit modifications of registration deviation caused by thermal distortions during launch. This study proposes a new image registration method based on the on-orbit detection of stars by using the stellar trajectory on a camera’s focal plane. A generated 12?×?12 data template and the Lagrange interpolation method are used in the registration model. Multispectral image registration based on stellar trajectory fitting can achieve high-precision image registration among different spectra.     

基于图像集拓扑中心的群体配准方法

传统的图像配准通常指定一幅参考图像在配准过程中保持不变,将另一幅图像变换到参考图像空间,使得两幅图像在空间上互相匹配,从而可以精确比较两者之间的差异.针对多幅个体差异较大的图像配准问题,如果指定一幅作为参考,将其他图像配准到参考图像空间,则会引入该幅参考图像的个体形状偏差,从而影响最终的对比结果.为此,本文首先介绍了目前针对该问题的主要解决方法,然后提出了基于图像集拓扑中心的群体配准方法——TopologyCenter.为验证所提出的群体配准方法的性能,通过使用国外公开的数据集,详细比较了本文提出的方法与当前两种主要方法的群体配准结果的差异.实验结果表明,本文提出的方法具有更小的群体配准偏差,群体配准结果更好;同时,在对实验结果的评价中,本文还提出了一种简捷的群体偏差度量指标.     

基于图像融合的动态轮廓线跟踪新方法

红外与可见光传感器是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融合能有效提高系统跟踪检测的准确性。将动态轮廓线模型与图像融合结合,在特征搜索过程中利用特征点准确地完成了图像配准,同时使用了一种新的特征级融合方法,将两种图像中目标轮廓的B样条曲线控制点进行实时微分耦合。这种耦合将Curwen提出的微分耦合机制作了改进,利用图像配准把刚性硬模板改变为实时的变换模板并推导了融合后动态轮廓线的新的动力学方程。这种融合利用了红外图像目标轮廓信息约束可见光图像中动态轮廓线的收敛形状,有效地提高了可见光图像目标跟踪的准确性。对运动人手序列图像的对比跟踪实验表明,这种融合使得可见光图像中动态轮廓线平均跟踪误差减小了60．25％。     

基于位相相关检测的图像配准算法

分析了传统的傅里叶-梅林变换和最近提出的虚拟极坐标傅里叶变换算法在户外图像注册中的局限性,提出了一种更加适合户外摄像机姿态注册的基于对数傅里叶变换的图像配准算法。该算法能够独立地计算沿两坐标轴方向的不同缩放因子和计算两图像间较大的旋转角度,扩展了基于傅里叶变换的图片配准算法的实际应用范围。在关键技术研究方面,提出了由升余弦函数构成的低通滤波器和一种快速收敛的角度搜索技术,使该算法达到了很高的效率和鲁棒性。     

Long-Term Interval Change Detection from a Sequence of Personal Images

Temporal series of personal images may provide us valuable information regarding progression of a disease over time. A new method, which is based on a pattern histogram approach and time series analysis, is presented to detect long-term interval changes. Highly correlated and increasing pattern characteristics are considered as real interval changes, while background and noisy patterns exhibit more chaotic variations over time. The method has been tested on a sequence of chest radiographs using corresponding regions of interest, which include an increasing number of small opacities. The experimental results show that the method is promising and can be used in clinical applications over time. A key aspect of the proposed method is its independence of feature locations; therefore, it does not require any image registration technique to extract feature characteristics of the interval changes.     

Study of mutual information multimodality medical image registration based on modified simplex optimization method

Because of a different imaging mechanism and highly complexity of body tissues and structures. Different modality medical images provide non-overlay complementary information. This has very important significance for multimodal medical image registration. Image registration is the first and key part of problem to be solved in the integrations. When the spatial position of two medical images is same, the registration could be achieved. For two CT and PET images, the principal axis method is adopted to achieve the rough registration. The modified simplex algorithm is employed to implement global search using the mutual information as similarity measure. The initial registration parameters are achieved through principal axis Based on the results of test, improved simplex method can adjust reflecting distance. Stepped-up optimization algorithm on the new experimental points through the methods of “reflection”, “enlargement”, “shrinkage” or “global systolic”. A mutual information registration based on modified simplex optimization method is presented in this paper to improve the speed of medical image registration.Results indicate that the proposed registration method prevents the optimizing process from falling into local extremum and improves the convergence speed while keeping the precision. The accurate registration of multimodal image with different resolutions is achieved.     

Automatic registration of serial sections of mouse lymph node by using Image-Reg

The first step towards the three-dimensional (3D) reconstruction of histological structures from serial sectioned tissue blocks is the proper alignment of microscope image sequences. We have accomplished an automatic rigid registration program, named Image-Reg, to align serial sections from mouse lymph node and Peyer's patch. Our approach is based on the calculation of the pixel-correlation of objects in adjacent images. The registration process is mainly divided into two steps. Once the foreground images have been segmented from the original images, the first step (primary alignment) is performed on the binary images of segmented objects; this process includes rotation by using the moments and translation through the X, Y axes by using the centroid. In the second step, the matching error of two binary images is calculated and the registration results are refined through multi-scale iterations. In order to test the registration performance, Image-Reg has been applied to an image and its transformed (rotated) version and subsequently to an image sequence of three serial sections of mouse lymph node. In addition, to compare our algorithm with other registration methods, three other approaches, viz. manual registration with Reconstruct, semi-automatic landmark registration with Image-Pro Plus and the automatic phase-correlation method with Image-Pro Plus, have also been applied to these three sections. The performance of our program has been also tested on other two-image data sets. These include: (a) two light microscopic images acquired by the automatic microscope (stitched with other software); (b) two images fluorescent images acquired by confocal microscopy (tiled with other software). Our proposed approach provides a fast and accurate linear alignment of serial image sequences for the 3D reconstruction of tissues and organs.     

Detection of Interval Changes from a Pair of Images: A Proposal of Pattern Histogram

A new method to detect interval changes from a pair of images is presented, especially when the precise image registration is not available. We extend a well-known image histogram to multi-dimensions with some modifications and make use of it to elucidate differences between the images. The framework herein introduced is based on a comparison of the images in their multi-dimensional histogram space rather than in a spatial domain. Due to its flexibility, this approach is preferred as an alternative image comparison technique. The performance of the method is confirmed on simulated and real medical images and compared with the results from related conventional counterparts.     

Magnetic resonance image enhancement using highly sparse input

Lately, the Magnetic Resonance scans have struggled with its own inherent limitations, such as spatial resolution as well as long examination times. In this paper, a novel, rapid compressively-sensed magnetic resonance high resolution image resolution algorithm is presented. This technique addresses these two key issues by employing a highly-sparse sampling scheme and super-resolution reconstruction (SRR) method. Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a discrete dense displacement sampling for the deformable human body and motion analysis. The clinical trials as well as phantom based studied have been conducted. It has been proven that the proposed algorithm is able to enhance image spatial resolution, reduce motion artefacts and scan times.     

多传感器图像自动配准技术研究

多传感器图像自动配准技术是军事领域里多传感器图像融合的必要前提。根据配准的步骤综述了现有的多传感器图像自动配准技术,以可见光和红外图像配准为例,提出采用小波变换、图像处理技术和人工智能技术相结合的方法来解决这一难题。