Contrast-independent curvilinear structure detection in biomedical images

Many biomedical applications require detection of curvilinear structures in images and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here, we propose a contrast-independent approach to identify curvilinear structures based on oriented phase congruency, i.e., the phase congruency tensor (PCT). We show that the proposed method is largely insensitive to intensity variations along the curve and provides successful detection within noisy regions. The performance of the PCT is evaluated by comparing it with state-of-the-art intensity-based approaches on both synthetic and real biological images.     

A hierarchical approach to feature extraction and grouping

In this paper, the problem of extracting and grouping image features from complex scenes is solved by a hierarchical approach based on two main processes: voting and clustering. Voting is performed for assigning a score to both global and local features. The score represents the evidential support provided by input data for the presence of a feature. Clustering aims at individuating a minimal set of significant local features by grouping together simpler correlated observations. It is based on a spatial relation between simple observations on a fixed level, i.e., the definition of a distance in an appropriate space. As the multilevel structure of the system implies that input data for an intermediate level are outputs of the lower level, voting can be seen as a functional representation of the "part-of" relation between features at different abstraction levels. The proposed approach has been tested on both synthetic and real images and compared with other existing feature grouping methods.     

基于张量投票的图像去噪算法研究

针对传统的基于张量投票算法图像去噪过程中对于对称图像存在的张量叠加计算量冗余的问题,提出了张量投票域的改进算法。首先,利用基于相位信息的图像对称性检测方法对需要去噪的图像进行检测。然后,根据检测出的图像的对称性的特性简化投票域的区域。最后,将改进后的投票域应用在图像去噪过程中。实验仿真的结果表明,这里的方法不仅明显提高了图像的峰值信噪比,而且较好地保护了图像的细节信息,在算法的效率方面也有较明显的提高。     

基于张量投票的空域错误隐藏算法

针对现有空域错误隐藏算法对复杂纹理恢复精度不高的问题,提出了一种基于张量投票的错误隐藏算法,利用张量投票提取的结构信息改善复杂纹理区域的恢复效果,并在此基础上结合传统算法提出了改进的空域错误隐藏算法,即首先对丢失宏块分类,然后根据丢失宏块的类型选取合适的隐藏算法。实验表明,该算法有效地提高了恢复精度,避免了边缘错乱现象,尤其在纹理较复杂的区域,能够使隐藏图像的主客观质量有较大提高。     

Road detection in spaceborne SAR images using a genetic algorithm

This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, the authors designed a grouping method based on a GA, which is a global optimization method. They combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. The authors applied their method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach     

Iterative reconstruction in X-ray fluorescence tomography based on radon inversion

We describe a new approach for the inversion of the generalized attenuated radon transform in X-ray fluorescence computed tomography (XFCT). The approach consists of using the radon inverse as an approximation for the actual one, followed by an iterative refinement. Also, we analyze the problem of retrieving the attenuation map directly from the emission data, giving rise to a novel alternating method for the solution. We applied our approach to real and simulated XFCT data and compared its performance to previous inversion algorithms for the problem, showing its main advantages: better images than those obtained by other analytic methods and much faster than iterative methods in the discrete setting.     

基于低秩张量补全的多声道音频信号恢复方法

多声道音频信号在采集、压缩、传输过程中可能造成音频数据丢失,为了确保给听众带来更真实的听觉感受,该文提出一种基于低秩张量补全的音频丢失数据恢复方法。首先,把多声道音频信号表示为一个张量;其次,把张量补全作为一个凸优化问题建模,利用松弛技术和变量分离技术得到闭合的增强拉格朗日函数;最后,通过交替迭代方法求解得到恢复的音频张量。在不同数据丢失率的实验中,通过与线性预测、加权优化的CANDECOMP /PARAFAC分解方法进行对比分析,表明利用张量补全方法具有更高的音频信号恢复精度,隐藏参考和基准的多激励测试结果也显示低秩张量补全方法能够有效地恢复多声道音频的丢失数据,从而获得更好的听觉效果。     

张量投票方法的投票域计算分析

张量投票方法是一种鲁棒性很强的特征提取方法,而投票域的计算则是应用该方法进行图像处理的关键步骤之一。通过实验分析了该方法的投票域计算时的唯一关键参数—尺度参数对投票结果的影响,并通过实例说明在具体应用领域中可以根据图像特点简化投票域的计算。     

Three-dimensional noisy image restoration using filtered extrapolation and deconvolution

The problem of restoration in fluorescence microscopy has to deal at the same time with blurring and photon noise. Their combined effects corrupt the image by inserting elements that do not belong to the real object and distort the contrast. This hinders the possibility of using the images for visualization, recognition, and analysis using the three-dimensional data. The algorithms developed to restore the lost frequencies and perform band extrapolation, in general, assume absence of noise or an additive noise. This paper presents a restoration approach through band extrapolation and deconvolution that deals with the noise. An extrapolation algorithm using constraints on both spatial and frequency domains with a smoothing operator were combined with the Richardson-Lucy iterative algorithm. The results of the method for simulated data are compared with those obtained by the original Richardson-Lucy algorithm and also regularized by Total Variation. The extrapolation of frequencies is also analyzed both in synthetic and in real images. The method improved the results with higher signal-to-noise ratio and quality index values, performing band extrapolation, and achieving a better visualization of the 3D structures.     

Gap filling of 3-D microvascular networks by tensor voting

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated.     

An adaptive tensor voting algorithm combined with texture spectrum

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.     

基于张量分解的双基地MIMO 雷达目标数估计

针对双基地多输入多输出(MIMO)雷达目标数估计问题,提出一种基于三阶张量分解的目标数估计算法。该算法首先将匹配滤波输出转化为三阶张量;然后,从张量的三个维度分别计算特征值,并通过迭代方法估计全局特征值;最后,利用全局特征值估计目标数。与传统基于矩阵分解算法相比,该算法充分利用了回波数据的多维结构特征,可有效改善特征值的估计精度,从而提高目标数的正确估计概率。仿真结果证明了所提算法的有效性和优越性。     

A waveguide-based two-step approach for measuring complex permittivity tensor of uniaxial composite materials

A rectangular waveguide-based two-step approach for measuring the complex permittivity tensor of uniaxial highly lossy nonmagnetic composite materials in the S-band is presented. In the proposed scheme, two independent sets of reflection and transmission coefficient data for each material-under-test (MUT) are measured by aligning the electric field vector of the dominant TE/sub 10/ mode in the rectangular waveguide parallel and perpendicular to the fiber orientation of the uniaxial sample, respectively. The complex permittivity tensor of the MUT is determined from these measured scattering data in two successive steps. The first step uses the newly proposed analytical approach, which can resolve the ambiguity problem, commonly encountered with samples of electrical length larger than a wavelength. In the second step, nonlinear least square optimization algorithms are employed, where the material parameters using the first step are now used as the initial guess. The proposed two-step approach is valid for multilayered structures, and the local minima problem commonly encountered with optimization routines are also avoided. A number of carbon-fiber composite materials along and, transverse to the fiber orientation are measured using the proposed method. Finally, a brief uncertainty analysis, to study the effect of air-gaps on waveguide measurements, is carried out.     

A Numerical Approach for Full-Vectorial Analysis of 3-D Guided Wave Structures With Multiple and Strong Longitudinal Discontinuities

In this work, an accurate and efficient iterative numerical approach suitable for the analysis of 3-D guided wave structures is presented. The proposed method is fully vectorial and based on the transverse magnetic field formulation. In order to enhance the computational efficiency of the proposed method, a hybrid implementation is utilized using Pade approximants in tandem with a reduced spectrum of eigenpairs. The present formulation can account for the presence of strong and multiple longitudinal discontinuities. Convergence of this iterative approach is achieved by the use of a simple preconditioner. The accuracy and efficiency of the proposed method are demonstrated by applying it to two different guided wave structures with multiple longitudinal discontinuities.     

一种改进的一致性扩散图像增强方法

一致性扩散是分析图像中定向结构的重要的预处理步骤,现有的一致性扩散图像增强方法难以识别图像中的弱边界。本文提出了一种改进的扩散方法,该方法构建了一种结合二阶方向导数信息的结构张量,能够精确分析图像中的复杂弱边界,同时与经典结构张量互为补充以检测图像的强边界。在此基础上设计了新的扩散张量,并采用了基于(加性分裂算子AOS)策略的数值计算方法。通过对比实验证明,该文算法的效果良好并具有很高的效率, 实现了在去除噪声的同时精确保护图像中的强弱边界。     

基于大数逻辑可译LDPC码的译码算法研究

本文提出两种基于可靠度的迭代大数逻辑译码算法,从以下两个方面降低译码复杂度:(1)校验节点使用伴随式信息处理,可节省外信息的计算操作;(2)变量节点使用伴随信息进行总信息的投票计数过程.结合非均匀量化技术,接收信号在判决门限附近获得更加精细的处理.此外,本文利用量化参数和列重比例信息对可靠度偏移方向和幅度进行了设计.仿真实验表明,本文提出的算法能够在很低的量化比特(3~4 bits)下有效工作,具有优良的译码性能和快速的收敛速度.     

Edge grouping combining boundary and region information.

This paper introduces a new edge-grouping method to detect perceptually salient structures in noisy images. Specifically, we define a new grouping cost function in a ratio form, where the numerator measures the boundary proximity of the resulting structure and the denominator measures the area of the resulting structure. This area term introduces a preference towards detecting larger-size structures and, therefore, makes the resulting edge grouping more robust to image noise. To find the optimal edge grouping with the minimum grouping cost, we develop a special graph model with two different kinds of edges and then reduce the grouping problem to finding a special kind of cycle in this graph with a minimum cost in ratio form. This optimal cycle-finding problem can be solved in polynomial time by a previously developed graph algorithm. We implement this edge-grouping method, test it on both synthetic data and real images, and compare its performance against several available edge-grouping and edge-linking methods. Furthermore, we discuss several extensions of the proposed method, including the incorporation of the well-known grouping cues of continuity and intensity homogeneity, introducing a factor to balance the contributions from the boundary and region information, and the prevention of detecting self-intersecting boundaries.     

Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

The problem of tracking multiple mobile targets, using a wireless sensor network, is investigated in this paper. We propose a new sensor grouping algorithm, based on the maximum sensor separation distances (G‐MSSD), for estimating the location of multiple indistinguishable targets, either jointly or individually, depending on the distances between the generated groups. The joint tracking algorithm is formulated as a maximum likelihood (ML) estimator and solved through a modified version of the well‐known Gauss‐Newton (MGN) iterative method. We propose two candidate initial guesses for MGN based on G‐MSSD in joint tracking mode, while for the individual mode, the information of each group is used to estimate the location of only the corresponding target. The Cramer‐Rao lower bound (CRLB) for the variance of the proposed ML estimator is derived, and the potential conditions for reducing the CRLB are presented. Since tracking efficiency is affected by poor estimates, we present two criteria to evaluate the quality of estimates and detect the poor ones. An approach is also proposed for correcting the poor estimates, based on additional initial guesses. We demonstrate the effectiveness and accuracy of our proposed dual‐mode algorithm via simulation results and compare our results with the Multi‐Resolution search algorithm.     

A multiresolution framework to MEG/EEG source imaging

A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from electroencephaloram (EEG)/magnetoencephalogram (MEG) signals is proposed in a distributed source model. At each step of the algorithm, a regularized solution to the inverse problem is used to constrain the source space on the cortical surface to be scanned at higher spatial resolution. We present the iterative procedure together with an extension of the ST-maximum a posteriori method [1] that integrates spatial and temporal a priori information in an estimator of the brain electrical activity. Results from EEG in a phantom head experiment with a real human skull and from real MEG data on a healthy human subject are presented. The performances of the multiresolution method combined with a nonquadratic estimator are compared with commonly used dipolar methods, and to minimum-norm method with and without multiresolution. In all cases, the proposed approach proved to be more efficient both in terms of computational load and result quality, for the identification of sparse focal patterns of cortical current density, than the fixed scale imaging approach.     

The complex bi-conjugate gradient solver applied to largeelectromagnetic scattering problems, computational costs, and costscalings

The complex bi-conjugate gradient iterative method is applied to an isoparametric boundary integral equation formulation for frequency-domain electromagnetic scattering problems. It is demonstrated to work well on large and geometrically complex examples, including a 20 wavelength slender dipole, the NASA almond, and a resonant cavity. On such problems, with asymmetric curvilinear irregular meshes and nontrivial geometries, the number of iterations required seems to increase rather more than linearly with body size, indicating an overall ~sixth power cost scaling. This scaling is essentially as for direct methods, but with costs still a small fraction of the direct approach. A method is proposed for the selection of a termination condition designed to avoid seeking the approximate answer too precisely; it typically permits a further halving of costs