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.     

Effect of nonrigid registration algorithms on deformation-based morphometry: a comparative study with control and Williams syndrome subjects

Deformation-based morphometry (DBM) is a widely used method for characterizing anatomical differences across groups. DBM is based on the analysis of the deformation fields generated by nonrigid registration algorithms, which warp the individual volumes to a DBM atlas. Although several studies have compared nonrigid registration algorithms for segmentation tasks, few studies have compared the effect of the registration algorithms on group differences that may be uncovered through DBM. In this study, we compared group atlas creation and DBM results obtained with five well-established nonrigid registration algorithms using 13 subjects with Williams syndrome and 13 normal control subjects. The five nonrigid registration algorithms include the following: (1) the adaptive bases algorithm, (2) the image registration toolkit, (3) The FSL nonlinear image registration tool, (4) the automatic registration tool, and (5) the normalization algorithm available in Statistical Parametric Mapping (SPM8). Results indicate that the choice of algorithm has little effect on the creation of group atlases. However, regions of differences between groups detected with DBM vary from algorithm to algorithm both qualitatively and quantitatively. Some regions are detected by several algorithms, but their extent varies. Others are detected only by a subset of the algorithms. Based on these results, we recommend using more than one algorithm when performing DBM studies.     

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.     

Separable convolution template (SCT) background prediction accelerated by CUDA for infrared small target detection

This paper presents a novel background prediction method for infrared small target detection (ISTD). Using a separable convolution template (SCT) to accelerate the traditional background prediction by graphic processing unit (GPU), the new method provides a significant improvement in the prediction speed, which enables the prediction process in real time. And experimental results show its high efficiency and practical application over previous work. The mathematical approach proposed here could be extended to accelerate the applications referred to image convolutions not only to the infrared field.     

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images

A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm²) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.     

Simulation of Properties of Images with Atomic Resolution in a Scanning Probe Microscope

A method for simulation of images in a scanning probe microscope (SPM) using simultaneous wavelet transform and median filtering is proposed. The wavelet transform with the fourth-order Daubechies kernel is used. Such a transform makes it possible to select details of different scales in the SPM image and, hence, study fractal properties of surfaces. Simulation is used to show that ultrahigh (atomic) resolution is possible in SPM provided that the size of the contact region in the probe–sample system is significantly greater than atomic size and the lattice atoms are randomly distributed. Contrast inversion in the SPM images in the multiscan mode is interpreted.     

How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study

PURPOSE: The purpose of this study was to determine a suitable registration algorithm for diffusion tensor imaging (DTI) using conventional preprocessing tools [statistical parametric mapping (SPM) and automated image registration (AIR)] and to investigate how anisotropic indices for clinical assessments are affected by these distortion corrections. MATERIALS AND METHODS: Brain DTI data from 15 normal healthy volunteers were used to evaluate four spatial registration schemes within subjects to correct image distortions: noncorrection, SPM-based affine registration, AIR-based affine registration and AIR-based nonlinear polynomial warping. The performance of each distortion correction was assessed using: (a) quantitative parameters: tensor-fitting error (Ef), mean dispersion index (MDI), mean fractional anisotropy (MFA) and mean variance (MV) within 11 regions of interest (ROI) defined from homogeneous fiber bundles; and (b) fiber tractography through the uncinate fasciculus and the corpus callosum. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated to demonstrate the effects of distortion correction. Repeated-measures analysis of variance was used to investigate differences among the four registration paradigms. RESULTS: AIR-based nonlinear registration showed the best performance for reducing image distortions with respect to smaller Ef (P<.02), MDI (P<.01) and MV (P<.01) with larger MFA (P<.01). FA was decreased to correct distortions (P<.0001) whether the applied registration was linear or nonlinear and was lowest after nonlinear correction (P<.001). No significant differences were found in MD. CONCLUSION: In conventional DTI processing, anisotropic indices of FA can be misestimated by noncorrection or inappropriate distortion correction, which leads to an erroneous increase in FA. AIR-based nonlinear distortion correction would be required for a more accurate measurement of this diffusion parameter.     

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.     

A least squares quantization table method for direct reconstruction of MR images with non-Cartesian trajectory

The direct Fourier transform method is a straightforward solution with high accuracy for reconstructing magnetic resonance (MR) images from nonuniformly sampled k-space data, given that the optimal density compensation function is selected and the underlying magnetic field is sufficiently uniform. The computation however is very time-consuming, making it impractical especially for large-size images. In this paper, the least squares quantization table (LSQT) method is proposed to accelerate the direct Fourier transform computation, similar to the recently proposed methods such as using look-up table (LUT) or equal-phase-line (EPL). With LSQT, all the image pixels are first classified into several groups where the Lloyd-Max quantization scheme is used to ensure the minimal classification error. The representative value of each group is stored in a small-size LSQT in advance to reduce the computational load. The pixels in the same group receive the same contribution, which is calculated only once for each group instead of for each pixel, resulting in the reduction of computation because the number of groups is far smaller than the number of pixels. Finally, each image pixel is mapped into the nearest group and its representative value is used to reconstruct the image. The experimental results show that the LSQT method requires far smaller memory size than the LUT method and fewer multiplication operations than the LUT and EPL methods. Moreover, the LSQT method can perform large-size reconstructions that achieve comparable or higher accuracy as compared to the EPL and gridding methods when the appropriate parameters are given. The inherent parallel structure also makes the LSQT method easily adaptable to a multiprocessor system.     

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。     

Two-step image registration by artificial immune system and chamfer matching

Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.     

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.     

Image improvement based on sub-pixel post-integration for a staring imaging system in geostationary orbit

This paper proposes the post-integration technology based on sub-pixel image registration and image fusion to improve the signal-to-noise ratio （SNR） of remote sensing images without motion degradation caused by satellite vibration. A two-dimensional vibration system is set up to simulate satellite disturbance. Image sequences with different exposure times are captured using a high-speed CMOS camera. The displacement plots are compared with the motion data measured by the grating linear encoder. These plots indicate that the accuracy of the registration algorithm is better than 0.1 pixels. The sub-pixel image fusion shows an improvement in image quality, thus indicating that this technology is powerful for staring imaging systems in geostationary orbit.     

海洋悬浮颗粒的光谱模拟:ADA与Mie的比较研究

基于海洋悬浮颗粒的折射率、粒径分布特征,利用异常衍射近似ADA(anomalous diffraction approximation)和米(Mie)散射方法进行衰减、吸收和散射效率的光谱模拟,分析两种方法计算结果相对偏差Δc,Δa,Δb及其影响因素,探讨利用计算速度占优的ADA方法进行海洋悬浮颗粒光谱模拟的可行性,研究发现:Δc,Δa,Δb受颗粒粒径、折射率实部和虚部的共同影响,且影响方式复杂;ADA方法满足计算精度(相对偏差小于5%)要求的最小颗粒半径Rmin随折射率和波长等的变化而变化。考虑到ADA与Mie的计算偏差受到多种因素的影响,而且海洋悬浮颗粒组分复杂多样,建议在进行海洋悬浮颗粒光谱模拟时,采用经典的Mie方法,而不是计算相对快速的ADA方法。     

增强现实技术在PDA上的应用

将增强现实技术与移动终端PDA相结合,在PDA上实现真实场景与虚拟增强信息的融合。系统采用基于Fourier_Mellin变换的2D图像匹配技术获取用户的视点参数,利用已标定的来自不同视点的图像作为参考图像库。采用客户/服务器构架减轻PDA的运算负担。研制的基于PDA移动终端的增强现实系统轻巧灵便,易于携带,通用性高,能够应用于户外场景。     

Effect of a charged scanned probe microscope tip on a subsurface electron gas

Using a cryogenic scanned probe microscope (SPM) one can locally modify the sheet density of a two-dimensional electron gas (2DEG), and image the ballistic flow of electrons through a point contact in the 2DEG located beneath the surface of a GaAs/AlGaAs heterostructure. We calculate the capacitively induced change in sheet density when a charged SPM tip is brought into contact with a semiconductor heterostructure containing a two-dimensional electron gas. A simple scattering model, based on a local change in sheet density, is used to analyze experimental data taken with the SPM method described above.     

基于多相组重建的航空图像超分辨率算法

为提高航空图像的空间分辨率, 提出一种基于多相组重建的超分辨率算法. 融合图像间的互补信息, 将多帧低分辨率图像作为图像基, 参考帧分解为多相组, 利用差异采样特性构建图像基与参考帧之间的的多相组线性关系重建得到高分辨率图像的多项组, 经图像多相分解逆变换获得融合的高分辨率图像. 根据该融合图像的局部内容和结构信息自适应调整控制核核函数, 应用改进的控制核回归算法去除图像模糊和噪声得到清晰的超分辨率图像. 与传统算法相比, 该算法无需图像配准和迭代过程, 计算效率极大地提高. 实验结果表明, 本文算法能够有效提高航空图像的空间分辨率, 在定量评价指标和主观视觉效果方面都有显著提高.     

Fast parallel Grad–Shafranov solver for real-time equilibrium reconstruction in EAST tokamak using graphic processing unit

To achieve real-time control of tokamak plasmas, the equilibrium reconstruction has to be completed sufficiently quickly. For the case of an EAST tokamak experiment, real-time equilibrium reconstruction is generally required to provide results within 1ms. A graphic processing unit(GPU) parallel Grad–Shafranov(G-S) solver is developed in P-EFIT code,which is built with the CUDA? architecture to take advantage of massively parallel GPU cores and significantly accelerate the computation. Optimization and implementation of numerical algorithms for a block tri-diagonal linear system are presented. The solver can complete a calculation within 16 μs with 65×65 grid size and 27 μs with 129×129 grid size, and this solver supports that P-EFIT can fulfill the time feasibility for real-time plasma control with both grid sizes.     

Rapid supersampling of multiframe sequences by use of blind deconvolution

Under certain conditions, multiframe image sequences can be processed to produce images that achieve greater resolution through image registration and increased sampling. This technique, known as supersampling, takes advantage of the spatiotemporal data available in an undersampled imaging sequence. In this effort the image registration is replaced by application of a fast blind-deconvolution technique to remove the motion blur in the upsampled average of the image sequence. This method produces a supersampled image with significantly decreased computational requirements compared with common methods. The method and simulated test results are presented.