采用整体最小二乘法的条纹图配准方法

针对共光路菲索型动态干涉仪采集到的4幅条纹图的空间一致性问题,本文提出一种新的条纹图配准思路,将条纹图与图像配准在关联度上人为分开,搭建了专用的图像配准装置,避免将条纹与十字丝标志点混在一幅图像内而影响干涉仪的测量精度。首先通过本文搭建的条纹图配准装置对4台CMOS相机进行物理配准,然后利用整体最小二乘法对采集到的同一十字丝刻划板图像进行十字丝提取、交点计算以及旋转量计算,实现共光路菲索型动态干涉仪条纹图像的点点对应。最后通过试验对比验证,证明了本文算法的配准精度优于模板重心法的配准结果,互相关度达到96%以上。     

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.     

利用统计特性进行医学图像配准效果定量评价

研究图像配准算法的客观效果评价。提出利用图像的统计特性,即均方根误差和交叉熵作为医学图像配准效果的定量评价指标,并以基于轮廓提取的图像配准算法和基于最大互信息的图像配准算法为例,就同一目标的两幅不同图像进行配准融合,对某一算法的配准效果、两种算法间的配准效果比较进行了初步的分析和评价。评价结果与理论分析结果、目视效果相吻合。统计特性法是一种客观和实用的对图像配准效果进行分析及评价的方法。     

基于Fourier-Mellin变换的气象卫星光谱图像配准

气象卫星光谱图像是气象科学和环境遥感科学研究的重要工具,而图像配准是气象卫星图像数据应用的前提。文章针对气象卫星光谱图像的配准问题,提出了一种基于Fourier-Mellin变换的自动配准方法。首先利用全球海岸线矢量图数据构造地标模板,地标模板是气象卫星光谱图像配准的参考图像;其次,根据云通道数据选择无云区域红外子图像,并利用Sobel算子对红外光谱图像提取边缘特征;最后利用Fourier-Mellin变换确定地标模板图像和红外边缘图像之间的仿射变换参数,从而实现红外光谱图像的配准。该方法本质是基于曲线匹配的思想,无需特征点提取,大大简化了配准流程。利用FY-2D气象卫星上获取的红外通道数据进行了实验,结果表明：该方法鲁棒性好,运算速度快,配准精度较高。     

一种高光谱图像高精度配准波段选择方法

针对高光谱图像和高空间分辨率图像配准过程中,各波段之间差异较大难以选择高精度配准波段的问题,提出一种基于Cram′er-Rao下限(CRLB)理论的高光谱图像高精度匹配波段选择算法。利用波段选择的方法选出高光谱图像中若干信息量大、相关性小的波段;将其分别与高空间分辨率图像做配准,并计算配准结果相应的CRLB;根据CRLB选择高精度配准波段。通过比较配准后的CRLB和均方根误差,验证CRLB具有较好配准质量评价性能。通过CRLB与其他方法的选择波段配准结果比较可知,本文算法选择的波段配准精度较高。上述波段为高光谱图像和高空间分辨率图像的配准提供更好的数据。     

基于图像处理技术的电能表外观检测系统

为适应智能电能表自动化检定的要求,设计了由图像处理技术完成外观检测功能的系统,实现了外观检查的自动化,解决了人工检测方法工作量大、效果不佳的问题。系统采用高性能CCD相机完成图像采集,确保图像信息丰富完整。利用图像平滑去噪、形态学处理等技术完成图像的预处理,大幅降低后续比对的难度。运用基于高斯金字塔的混合配准算法完成图像配准,最终实现待检图像与模板图像之间匹配程度的检测。该方法检测时间短,正确率高,已成功应用于省级计量中心电能表自动化检定线,满足生产需求。     

幅值和相位配准技术及其在光学相干层析血流成像中的应用

频域光学相干层析系统中扫描机构定位精度、 机械抖动及样品移动会造成A扫描信号幅值和相位发生波动, 影响生物组织成像质量. 利用最小灰度差匹配、Lorentzian曲线极值拟合和谱域光程差补偿等方法对A 扫描信号进行幅值配准. 通过对A扫描信号相位分布特征的匹配实现相位差检测与配准. 通过求已配准的A 扫描复信号之差, 消除静态组织对血流成像的影响. 进行了人眼扫描实验, 有效提取了视网膜三维血流图像. 实验结果表明, 提出的幅值及相位配准方法大大减小了系统扫描精度、人眼跳动等因素对生物组织在体成像质量的影响. 快速、精确的相位配准方法也可广泛应用在多普勒OCT、相位显微等与相位分辨有关的光学成像领域. 关键词： 频域光学相干层析 配准 血流成像 相位分布特征     

基于角点的红外与可见光图像自动配准方法

针对红外图像与可见光图像的自动配准问题,提出了一种基于图像角点特征以及仿射变换模型的方法.利用Harris因子分别在红外图像和可见光图像上检测角点,并对两幅图像进行边缘检测,得到其边缘图像.通过角点邻域在边缘图像上的相关性,实现角点的粗匹配;通过角点的细匹配,从匹配的角点中选择两对匹配最佳的点作为仿射变换的控制点,得到仿射变换模型,并对待配准图像进行仿射变换,从而实现图像配准.实验结果表明:该方法运算速度快,可以很好地完成红外与可见光图像的自动配准.     

基于仿射变换与Levenberg-Marquardt算法的织物图像配准

针对多光谱成像系统采集的织物图像的颜色色差分析问题,提出了一种基于仿射变换与LevenbergMarquardt(LM)算法的图像配准方法。从配准角度出发,利用提出的配准方法将标样图像与打样图像配准后进行空间色差分析。多光谱系统采集的织物图像的形变,包括平移、旋转、缩放和错切变换,符合典型仿射变换模型。提出一种新的方法来估计仿射变换矩阵,该方法对两幅图像的对数极坐标幅度谱积分曲线进行匹配,将仿射变换矩阵求解转化为一个非线性最小二乘拟合问题,进而利用LM算法搜寻最优参数值,同时引入分块配准以得到更好的配准效果。实验结果表明,与传统基于Fourier-Mellin配准算法和基于尺度不变特征变换的特征点配准算法相比,提出的算法可获得更好的配准效果,可有效解决具有周期性元素的织物图像配准问题,有助于织物图像色差评估。     

基于相位一致性和Hough变换的多源图像配准方法

由于红外图像与可见光图像对比度不同,常用基于梯度幅值的特征匹配方法难以正确配准。在分析红外图像与可见光图像成像机制的基础上,提出了一种结合相位一致性边缘检测与Hough变换的多源图像配准新方法。该算法首先采用高通滤波和平台直方图均衡方法对红外图像进行预处理以提高红外图像的对比度,再利用具有图像对比度不变性的相位一致性边缘检测法提取两幅图像的边缘,结合Hough变换选取图像空间中最长的线作为特征,采用改进相位相关法作为相似性度量,在对数极坐标域下计算出两幅图像的几何变形参数。仿真实验结果表明,该方法能够以较高查准率实现红外与可见光图像自动配准,并具有较强的鲁棒性。     

CORSICA: correction of structured noise in fMRI by automatic identification of ICA components

When applied to functional magnetic resonance imaging (fMRI) data, spatial independent component analysis (sICA), a data-driven technique that addresses the blind source separation problem, seems able to extract components specifically related to physiological noise and brain movements. These components should be removed from the data to achieve structured noise reduction and improve any subsequent detection and analysis of signal fluctuations related to neural activity. We propose a new automatic method called CORSICA (CORrection of Structured noise using spatial Independent Component Analysis) to identify the components related to physiological noise, using prior information on the spatial localization of the main physiological fluctuations in fMRI data. As opposed to existing spectral priors, which may be subject to aliasing effects for long-TR data sets (typically acquired with TR >1 s), such spatial priors can be applied to fMRI data, regardless of the TR of the acquisitions. By comparing the proposed automatic selection to a manual selection performed visually by a human operator, we first show that CORSICA is able to identify the noise-related components for long-TR data with a high sensitivity and a specificity of 1. On short-TR data sets, we validate that the proposed method of noise reduction allows a substantial improvement of the signal-to-noise ratio evaluated at the cardiac and respiratory frequencies, even in the gray matter, while preserving the main fluctuations related to neural activity.     

Evaluation of atlas-based segmentation of hippocampi in healthy humans

Introduction and aim

Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus.

Material and methods

High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/).

Results

The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas.

Conclusion

Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis.     

Real-time automatic registration in optical surgical navigation

An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.     

Image registration framework for large-scale longitudinal MRI data sets: strategy and validation

Advanced magnetic resonance imaging (MRI) studies often require the transformation of large numbers of images into a common space. Calculating transformations that relate each image to every other and applying them to the images on demand are theoretically possible; however, these can be computationally prohibitive. Therefore, relating each image to only one other image, then linking those transforms together to relate any two images in the database, may be an efficient alternative. Evaluated were the feasibility and validity of image registration to bring intraindividual MR images into mutual correspondence for longitudinal analysis through the concatenation of precomputed transforms. A longitudinal data set of 10 multiple sclerosis patients with nine serial dual-echo spin-echo, 1.5-T MRI scans was used. Intrasubject registrations were performed stepwise between consecutive images and direct from each time point to the baseline. Consecutive transforms were concatenated and evaluated against direct registrations by comparing the resulting transformed images (using Pearson correlation coefficient). Confounding variables such as time between scans, brain atrophy, and change in lesion load were evaluated. We found the images resampled with the direct and the concatenated transforms to be highly correlated, and there was no significant difference between methods. Differences in brain parenchymal fraction (a measure of brain atrophy) showed significant inverse correlation with the correspondence of the resampled images. Results indicate that concatenating multiple transforms that link two images together produces near-identical results to that of direct registration; thus, this method is both useful and valid.     

Multicontrast multiecho FLASH MRI for targeting the subthalamic nucleus

The subthalamic nucleus (STN) is one of the most common stimulation targets for treating Parkinson's disease using deep brain stimulation (DBS). This procedure requires precise placement of the stimulating electrode. Common practice of DBS implantation utilizes microelectrode recording to locate the sites with the correct electrical response after an initial location estimate based on a universal human brain atlas that is linearly scaled to the patient's anatomy as seen on the preoperative images. However, this often results in prolonged surgical time and possible surgical complications since the small-sized STN is difficult to visualize on conventional magnetic resonance (MR) images and its intersubject variability is not sufficiently considered in the atlas customization. This paper proposes a multicontrast, multiecho MR imaging (MRI) method that directly delineates the STN and other basal ganglia structures through five co-registered image contrasts (T1-weighted navigation image, R2 map, susceptibility-weighted imaging (phase, magnitude and fusion image)) obtained within a clinically acceptable time. The image protocol was optimized through both simulation and in vivo experiments to obtain the best image quality. Taking advantage of the multiple echoes and high readout bandwidths, no interimage registration is required since all images are produced in one acquisition, and image distortion and chemical shift are reduced. This MRI protocol is expected to mitigate some of the shortcomings of the state-of-the-art DBS implantation methods.     

Accuracy of image registration between MRI and light microscopy in the ex vivo brain

A multistep procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin-stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324±0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).     

3-D imaging and modeling – Part I: acquisition and registration

The key modules in a typical reverse engineering system consist of the acquisition, registration, and the integration of range images of three-dimensional objects with complex geometry. We first present a self-made optical digitizer that is employed for quickly acquiring range data from multiple views, then introduce a visualized registration procedure that can provide a reliable estimate for the fine registration of multiple range images in a unified coordinate system. Experiment results are given to show the effectiveness of this approach in 3-D imaging and multiple range image registration for the applications of reverse engineering.     

3-D imaging and modeling – Part I: acquisition and registration

The key modules in a typical reverse engineering system consist of the acquisition, registration, and the integration of range images of three-dimensional objects with complex geometry. We first present a self-made optical digitizer that is employed for quickly acquiring range data from multiple views, then introduce a visualized registration procedure that can provide a reliable estimate for the fine registration of multiple range images in a unified coordinate system. Experiment results are given to show the effectiveness of this approach in 3-D imaging and multiple range image registration for the applications of reverse engineering.     

Using nine degrees-of-freedom registration to correct for changes in voxel size in serial MRI studies

Quantitative longitudinal brain magnetic resonance (MR) studies may be confounded by scanner-related drifts in voxel sizes. Total intracranial volume (TIV) normalisation is commonly used to correct serial cerebral volumetric measurements for these drifts. We hypothesised that automated rigid-body registration of whole brain incorporating automatic scaling correction might also correct for such fluctuations, and might be a more practical alternative. Twenty-three subjects (12 patients with Alzheimer's disease [AD] and 11 controls) had at least two serial T1-weighted volumetric brain MR scans. Ten scans from the control subjects were artificially scaled (stretched) by 1.5, 3.0, 4.6 and 6.1%. A 9-degrees-of-freedom (9dof) registration was used to register the scaled scans back onto the original scans and corresponding scaling factors compared to TIV measurements. A further nine 1-year repeat scans from the AD subjects were artificially scaled and registered (9dof) to baseline. The two correction methods were further assessed using multiple serial scans for each of the 23 subjects (resulting in 49 scan pairs). All serial scans were registered (9dof) to baseline. TIV was measured on all scans. It was found that the 9dof registration successfully recovered the artificially generated scaling changes. Scaling correction using 9dof registration did not alter the amount of brain atrophy measured over the 1-year period in the AD subjects. The 9dof volume scaling factors were very similar to the TIV ratios (repeat TIV over baseline TIV), but less variable (p < 0.001), in both artificial and 'real' scenarios. In the latter, the volume scaling factors allowed identification of two time-points in which a 3% change in voxel size had occurred. Both the 9dof brain registration and TIV correction were successfully able to correct for these fluctuations. Significant shifts in voxel size are a problem in longitudinal brain imaging studies. It is important that such changes are adjusted for: 9dof registration, which is automated and computationally inexpensive, may be superior to the more labour-intensive TIV correction for this purpose.     

Registering and analyzing rat fMRI data in the stereotaxic framework by exploiting intrinsic anatomical features

The value of analyzing neuroimaging data on a group level has been well established in human studies. However, there is no standard procedure for registering and analyzing functional magnetic resonance imaging (fMRI) data into common space in rodent fMRI studies. An approach for performing rat imaging data analysis in the stereotaxic framework is presented. This method is rooted in the biological observation that the skull shape and size of rat brain are essentially the same as long as their weights are within certain range. Registration is performed using rigid-body transformations without scaling or shearing, preserving the unique properties of the stable shape and size inherent in rat brain structure. Also, it does not require brain tissue masking and is not biased towards surface coil sensitivity profile. A standard rat brain atlas is used to facilitate the identification of activated areas in common space, allowing accurate region of interest analysis. This technique is evaluated from a group of rats (n=11) undergoing routine MRI scans; the registration accuracy is estimated to be within 400 μm. The analysis of fMRI data acquired with an electrical forepaw stimulation model demonstrates the utility of this technique. The method is implemented within the Analysis of Functional NeuroImages (AFNI) framework and can be readily extended to other studies.