基于三维激光扫描的物体重构建模

针对锂离子电池SOC(荷电状态)难以估算的问题,通过对电池建立等效的Thevenin电路模型,对不同时刻的SOC的模型参数进行拟合得到动态的模型参数,在Matlab中借助Simulink建立仿真模型,采用模块化结构,建立基于卡尔曼滤波算法的电池SOC估算系统;利用测得的电池电压电流,仿真系统可直接估算出实时的电池SOC,与实际的电池SOC对比,误差保持在2.5%以内,表明该方法可以有效的估计电池的SOC,对于锂离子电池在实际应用的容量估算有着重要意义。     

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).     

高光谱RX异常检测的多DSP并行化处理技术

为达到高光谱图像数据RX异常检测处理的高速、实时、海量存储等要求,本文提出了一种基于 CPCI Express 标准总线架构的多DSP高光谱图像并行处理系统的解决方案。系统采用4片DSP共享数据总线和存储器的紧耦合与Link口互联相结合的硬件拓扑架构。在该硬件平台上,针对光谱RX异常检测算法以及光谱图像三维数据的特点,分配各DSP并行处理任务,提出了一种利用图像空间分块计算并求整个图像的均值矩阵与协方差矩阵的4DSP并行处理技术。结果表明,在保证同等探测效果的条件下,采用本文的RX异常检测算法4DSP并行处理技术,可以达到单DSP处理4倍的时间效率,解决了DSP内存容量对大数据量图像处理的限制,并较好的完成了光谱数据的实时处理要求。     

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.     

利用三维自适应分裂-合并的MRI图像分割算法设计

该文提出一种基于3D几何特征分裂-合并(ASM)的脑部MRI图像分割算法。首先构建简单平行六面体的12种3D区域分割策略,体积分割技术将整个体积划分为许多大的均匀三维几何区;然后,在体积内定义更多小的均匀区域,以便在随后的合并步骤中有更大的生存概率;最后,进行多级区域合并,合并阶段只涉及复杂ASM树的叶子,考虑灰度相似性和共同边界区的大小,将小的区域合并为大邻近区。相比其他几种MRI图像分割算法,提出的方法在分割过程对噪声具有鲁棒性,提高了分割性能和准确率。另外提出的方法不需要训练数据集。     

Registration and integration algorithm in structured light three-dimensional scanning based on scale-invariant feature matching of multi-source images

Based on the homography between a multi-source image and three-dimensional (3D) measurement points, this letter proposes a novel 3D registration and integration method based on scale-invariant feature matching. The matching relationships of two-dimensional (2D) texture gray images and two-and-a-half- dimensional (2.5D) range images are constructed using the scale-invariant feature transform algorithms. Then, at least three non-collinear 3D measurement points corresponding to image feature points are used to achieve a registration relationship accurately. According to the index of overlapping images and the local 3D border search method, multi-view registration data are rapidly and accurately integrated. Experimental results on real models demonstrate that the algorithm is robust and effective.     

Temperature dependent structural changes in liquid benzene studied using neutron diffraction

We present neutron diffraction data for liquid benzene measured as a function of temperature from 10°C to 70°C. Isotopic substitution and Empirical Potential Structure Refinement were used to obtain a complete spatial and orientational understanding of nearest neighbour correlations. We find an increased prevalence of nearest neighbour molecules where the angle between the aromatic planes is perpendicular, and at an angle of around 60°, when the temperature is reduced. There is no significant change in prevalence parallel stacked arrangements of nearest neighbour molecules as a function of temperature. The structure of the increased local order is consistent with structural motifs observed in benzene dimers and clusters from quantum chemistry calculations in the literature.     

Bias field reduction by localized Lloyd-Max quantization

Bias field reduction is a common problem in medical imaging. A bias field usually manifests itself as a smooth intensity variation across the image. The resulting image inhomogeneity is a severe problem for posterior image processing and analysis techniques such as registration or segmentation. In this article, we present a novel debiasing technique based on localized Lloyd-Max quantization (LMQ). The local bias is modeled as a multiplicative field and is assumed to be slowly varying. The method is based on the assumption that the global, undegraded histogram is characterized by a limited number of gray values. The goal is then to find the discrete intensity values such that spreading those values according to the local bias field reproduces the global histogram as good as possible. We show that our method is capable of efficiently reducing (even strong) bias fields in 3D volumes.     

Segmentation of holographic images using the level set method

Image segmentation, which is to distinguish objects from background, has played an important role in holographic image processing. In this work, we propose a technique that uses the combination of rough segmentation and the level set methods for segmentation of holographic images with zero-order diffraction interference. To improve the image quality, we eliminate the interference of zero-order diffraction in digital holography using Sobel differential gradient algorithm based on digital image processing. After reconstruction, the rough segmentation including intensity transformations and Morphology methods is applied to acquire the rough contour of the reconstruction image. Finally, we adopt level set methods to smooth the object contour and improve its accuracy. Segmentation results for objects in holographic images are presented.     

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties.     

3D image registration using a fast noniterative algorithm

This note describes the implementation of a three-dimensional (3D) registration algorithm, generalizing a previous 2D version [Alexander, Int J Imaging Systems and Technology 1999;10:242-57]. The algorithm solves an integrated form of linearized image matching equation over a set of 3D rectangular sub-volumes ('patches') in the image domain. This integrated form avoids numerical instabilities due to differentiation of a noisy image over a lattice, and in addition renders the algorithm robustness to noise. Registration is implemented by first convolving the unregistered images with a set of computationally fast [O(N)] filters, providing four bandpass images for each input image, and integrating the image matching equation over the given patch. Each filter and each patch together provide an independent set of constraints on the displacement field derived by solving a set of linear regression equations. Furthermore, the filters are implemented at a variety of spatial scales, enabling registration parameters at one scale to be used as an input approximation for deriving refined values of those parameters at a finer scale of resolution. This hierarchical procedure is necessary to avoid false matches occurring. Both downsampled and oversampled (undecimating) filtering is implemented. Although the former is computationally fast, it lacks the translation invariance of the latter. Oversampling is required for accurate interpolation that is used in intermediate stages of the algorithm to reconstruct the partially registered from the unregistered image. However, downsampling is useful, and computationally efficient, for preliminary stages of registration when large mismatches are present. The 3D registration algorithm was implemented using a 12-parameter affine model for the displacement: u(x) = Ax + b. Linear interpolation was used throughout. Accuracy and timing results for registering various multislice images, obtained by scanning a melon and human volunteers in various stationary positions, is described. The algorithm may be generalized to more general models of the displacement field, and is also well suited to parallel processing.     

基于特征点匹配的图像配准算法精度提升方法研究

图像配准是多种图像后续处理的基础,较为常见的有图像融合,图像拼接、图像的三维重建等,这些后续操作都需要在一个好的配准前提下才能完成,因此,对于图像配准精度改进的研究具有很重要的实际应用价值。对基于特征点匹配的图像配准算法提出了几个配准精度提升的方法,这些方法分别针对特征检测精度的提升和特征匹配精度的提升来达到图像配准精度提升的目的。     

Two-dimensional Otsu's thresholding segmentation method based on grid box filter

Otsu algorithm, an automatic thresholding method, is widely used in classic image segmentation applications. In this paper, a novel two-dimensional (2D) Otsu thresholding algorithm based on local grid box filter is proposed. In our method, firstly by utilizing the coarse-to-fine idea, the 2D histogram is divided into regions by grid technique, and each region is used as a point to form a new 2D histogram, to which 2D Otsu thresholding algorithm and an improved particle swarm optimization (PSO) algorithm are applied to get the region number of the new 2D histogram threshold. Then on the result region, the mean of the 2D histogram is computed base on box filter, and the two algorithms are applied again to obtain the final threshold for the original image. Experimental results on real data show that the proposed algorithm gets better segmentation results than the traditional recursion Otsu algorithm. It significantly reduces the time of segmentation process and simultaneously has the higher segmentation accuracy.     

Energy scan photoelectron diffraction: An integrated method for adsorbate structure determinations

In order to determine the structure of adsorbates on surfaces an integrated multi-step approach has been developed for the analysis of energy scan photoelectron diffraction data. The first step of the method gives an approximate three-dimensional image of the dominant nearest neighbour scatterers surrounding an emitter atom. In a second step this approximate structure is further refined using multiple scattering cluster calculations. Finally, a very recently developed procedure is employed in which the dynamics/displacements of the emitter atom are examined utilising the Maximum Entropy Method. The application of this integrated method is illustrated with examples of molecular adsorbates.     

Registering spherical navigators with spherical harmonic expansions to measure three-dimensional rotations in magnetic resonance imaging

Subject motion remains a challenging problem to overcome in clinical and research applications of magnetic resonance imaging (MRI). Subject motion degrades the quality of MR images and the integrity of experimental data. A promising method to correct for subject motion in MRI is the spherical navigator (SNAV) echo. Spherical navigators acquire k-space data on the surface of a sphere in order to measure three-dimensional (3D) rigid-body motion. Analysis begins by registering the magnitude of two SNAVs to determine the 3D rotation between them. Several different methods to register SNAV data exist, each with specific capabilities and limitations. In this study, we assessed the accuracy, precision and computational requirements of measuring rotations about all three coordinate axes by correlating the spherical harmonic expansions of SNAV data. We compare the results of this technique to previous SNAV studies and show that, although computationally expensive, the spherical harmonic technique is a highly accurate, precise and robust method to register SNAVs and detect 3D rotations in MRI. A key advantage to the spherical harmonic technique is the ability to optimize the accuracy, precision, processing time and memory requirements by adjusting parameters used in the registration. While present developments are aimed at improving the programming efficiency and memory handling of the algorithm, this registration technique is currently well suited for retrospective motion correction applications, such as removing motion-related image artifacts and aligning slices within a high-resolution 3D volume.     

Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques

Objective

3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved.

Methods

Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated.

Results

The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1 mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6 mm.

Conclusion

TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.     

Monte carlo calculations near a uniaxial Lifshitz point

The Monte Carlo method is applied to a threedimensional Ising model with nearest neighbour ferromagnetic interactions and next nearest neighbour antiferromagnetic interactions along one axis only. Special emphasis is given to the critical behaviour near the Lifshitz point.Some parts of this paper have been presented at the 13th International Conference on Statistical Physics, Haifa, August 1977     

Volume quantification by fuzzy logic modelling in freehand ultrasound imaging

Introduction

Many algorithms exist for 3D reconstruction of data from freehand 2D ultrasound slices. These methods are based on interpolation techniques to fill the voxels from the pixels. For quantification purposes, segmentation is involved to delineate the structure of interest. However, speckle and partial volume effect errors can affect quantification.

Objective

This study aimed to assess the effect of the combination of a fuzzy model and 3D reconstruction algorithms of freehand ultrasound images on these errors.

Methods

We introduced a fuzzification step to correct the initial segmentation, by weighting the pixels by a distribution function, taking into account the local gray levels, the orientation of the local gradient, and the local contrast-to-noise ratio. We then used two of the most wide-spread reconstruction algorithms (pixel nearest neighbour (PNN) and voxel nearest neighbour (VNN)) to interpolate and create the volume of the structure. Finally, defuzzification was used to estimate the optimal volume.

Validation

B-scans were acquired using 5 MHz and 8 MHz ultrasound probes on ultrasound tissue-mimicking phantoms. Quantitative evaluation of the reconstructed structures was done by comparing the method output to the real volumes. Comparison was also done with classical PNN and VNN algorithms.

Results

With the fuzzy model quantification errors were less than 4.3%, whereas with classical algorithms, errors were larger (10.3% using PNN, 17.2% using VNN). Furthermore, for very small structures (0.5 cm³), errors reached 24.3% using the classical VNN algorithm, while they were about 9.6% with the fuzzy VNN model.

Conclusion

These experiments prove that the fuzzy model allows volumes to be determined with better accuracy and reproducibility, especially for small structures (<3 cm³).     

Analytical solution of 1D Ising-like systems modified by weak long range interaction

It is well-known that 1D systems with only nearest neighbour interaction exhibit no phase transition. It is shown that the presence of a small long range interaction treated by the mean field approximation in addition to strong nearest neighbour interaction gives rise to hysteresis curves of large width. This situation is believed to exist in spin crossover systems where by the deformation of the spin changing molecules, an elastic coupling leads to a long range interaction, and strong bonding between the molecules in a chain compound leads to large values for nearest neighbour interaction constants. For this interaction scheme an analytical solution has been derived and the interplay between these two types of interaction is discussed on the basis of experimental data of the chain compound which exhibits a very large hysteresis of 50 K above RT at 370 K. The width and shape of the hysteresis loop depend on the balance between long and short range interaction. For short range interaction energies much larger than the transition temperature the hysteresis width is determined by the long range interaction alone. Received 26 November 1998     

Three-dimensional segmentation of anatomical structures in MR images on large data bases

In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures over large data bases of 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D-region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D-regions are identified. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. Then, the decision stage intends to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. The method has been applied on a data base of 3D brain MRIs composed of fifty patients. Results are illustrated by segmenting the ventricles, corpus callosum, cerebellum, hippocampus, pons, medulla and midbrain on our data base and the approach is validated on two phantom 3D MRIs.