Multiresolution imaging in elastography

The range of strains that can be imaged by any practical elastographic imaging system is inherently limited, and a performance measure is valuable to evaluate these systems from the signal and noise properties of their output images. Such a measure was previously formulated for systems employing cross-correlation based time-delay estimators through the strain filter. While the strain filter predicts the signal-to-noise ratio (SNR(e)) for each tissue strain in the elastogram and provides valuable insights into the nature of image noise, it understated the effects of image resolution (axial resolution, as determined by the cross-correlation window length) on the noise. In this work, the strain filter is modified to study the strain noise at multiple resolutions. The effects of finite window length on signal decorrelation and on the variance of the strain estimator are investigated. Long-duration windows are preferred for improved sensitivity, dynamic range, and SNR(e). However, in this limit the elastogram is degraded due to poor resolution. The results indicate that for nonzero strain, a window length exists at which the variance of strain estimator attains its minima, and consequently the elastographic sensitivity, dynamic range and SNR(e) are strongly affected by the selected window length. Simulation results corroborate the theoretical results, illustrating the presence of a window length where the strain estimation variance is minimized for a given strain value. Multiresolution elastography, where the strain estimate with the highest SNR(e) obtained by processing the pre- and post-compression waveforms at different window lengths is used to generate a composite elastogram and is proposed to improve elastograms. All the objective elastogram parameters (namely: SNR(e), dynamic range, sensitivity and the average elastographic resolution-defined as the cross-correlation window length) are improved with multiresolution elastography when compared to the traditional method of utilizing a single window length to generate the elastogram. Experimental results using a phantom with a hard inclusion illustrates the improvement in elastogram obtained using multiresolution analysis.     

Phase rotation methods in filtering correlation coefficients for ultrasound speckle tracking

In speckle-tracking-based myocardial strain imaging, large interframe/volume peak-systolic strains cause peak hopping artifacts separating the highest correlation coefficient peak from the true peak. A correlation coefficient filter was previously designed to minimize peak hopping artifacts. For large strains, however, the correlation coefficient filter must follow the strain distribution to remove peak hopping effectively. This processing usually means interpolation and high computational load. To reduce the computational burden, a narrow band approximation using phase rotation is developed in this paper to facilitate correlation coefficient filtering. Correlation coefficients are first phase rotated to increase coherence, then filtered. Rotated phase angles are determined by the local strain and spatial position. This form of correlation coefficient filtering enhances true correlation coefficient peaks in large strain applications if decorrelation due to deformation does not completely destroy the coherence among neighboring correlation coefficients. The assumed strain used in the filter can also deviate from the true strain and still be effective. Further improvement in displacement estimation can be expected by combining correlation coefficient filtering with a new Viterbi-based displacement estimator.     

Optimization of speed-of-sound estimation from noisy ultrasonic signals

The effects of prefiltering and the choice of time-delay estimators and statistical data reduction techniques on the precision of speed-of-sound estimation were investigated using the beam-tracking technique. It was found that prefiltering the data with an ideal 50-kHz low-pass filter improved the precision of the estimation in all cases. Echo cross-correlation had an advantage over peak detection for low signal-to-noise ratio (SNR) levels, but its advantage diminished as the signal-to-noise level improved due to filtering. The linear regression method was superior to the paired-point analysis technique under all conditions. Using the optimal set of parameters, precision on the order of 0.1% was achieved in a tissue-mimicking phantom when one beam was tracking along 75 mm in 1-mm increments.     

Enhancement of echo-signal correlation in elastography using temporal stretching

Echo-signal decorrelation due to tissue compression is a significant source of error in tissue displacement estimates obtained using crosscorrelation. Tissue displacement estimates are used to compute strain values for imaging the elasticity of biological soft tissues. The correlation coefficient between the pre- and post-compression echo rf signals reduces rapidly with signal decorrelation due to increased compression. Miniscule reductions in the value of the correlation coefficient can have a significant impact on the performance of the strain estimator as illustrated by the strain filter. Reducing the rate of signal decorrelation using temporal stretching (which improves the value of the correlation coefficient), significantly improves the performance of the strain filter. The reduction in the rate of signal decorrelation with the subsequent increase in the correlation coefficient using temporal stretching is discussed in this paper. Theoretical, simulation and experimental results quantify the enhancement in the value of the correlation coefficient attained with temporal stretching.     

基于自适应因子化 H∞滤波的单站无源跟踪

针对单站无源跟踪系统非线性较强、传统跟踪滤波方法收敛速度慢且容易发散的问题,提出了一种基于自适应因子化 H∞滤波的单站无源跟踪算法.该算法利用 sigma 点转换和鲁棒 H∞滤波能够减小观测方程的线性化误差和降低观测误差不确定性的特点,通过新息控制减小野值对滤波的干扰,利用比例因子和渐消因子自适应调整采样点到中心点的距离和状态预报误差的协方差,从而克服基于 UT 变换的 H∞滤波采样时的非局部效应问题,增强了单站无源跟踪系统对噪声的鲁棒性.仿真实验结果表明,本文方法通过对 UT 变换进行简化,在自适应因子化的同时,算法的计算量与基于 UT 变换的 H∞滤波基本持平,且跟踪精度优于基于 UT 变换的 H∞滤波算法.该算法在保持高精度估计能力的同时,具有较强的鲁棒性,是解决非线性系统状态估计问题的一种有效方法.     

Vessel wall detection and blood noise reduction in intravascularultrasound imaging

Scattering from blood limits the contrast between the vessel wall and the lumen in intravascular ultrasound imaging. This makes it difficult to localize the vessel wall, especially on still images. This paper presents a method for automatic detection of vessel walls and reduction of blood noise based on correlation of the RF-signal between adjacent frames. The ultrasound RF-signal is quadrature demodulated, digitized, stored in memory, and transferred to a computer for processing and analysis. The absolute value of the cross-correlation coefficient between two adjacent frames is used to differentiate between stationary and fluctuating signals. Models and numerical calculations presented in this work indicate that the cross-correlation coefficient obtained from a radially dilating vessel wall will be larger than 0.8 under standard 20 MHz imaging conditions. The corresponding value from blood is less than 0.2 for blood velocities exceeding 0.5 cm s^-1 . The blood-noise filter is based on detecting this difference in correlation and displays vessel wall regions with no modifications, while regions detected as blood are rejected. A simplified vessel-wall detector that is suitable for real-time implementation is proposed. The performance of this detector and the blood noise filter are demonstrated by in vitro experiments     

被动定向浮标阵卡尔曼滤波目标跟踪研究

文章研究利用被动定向浮标阵定位跟踪水下机动目标的方法,基于卡尔曼滤波(Kalman Filter, KF)原理提出一种定位跟踪滤波器的具体实现方法。该方法能够整合多枚浮标现在及过去有误差的测量数据,提高定位精度,同时连续输出水下目标运动参数估计从而锁定目标运动轨迹。该方法实现的关键在于建立水下目标与浮标阵的数学迭代运算模型,包括状态空间的动态与观测过程。由于被动定向浮标阵目标跟踪是一个非线性估计问题,而卡尔曼滤波器是线性的,因此文章设计了近似的线性观测方程以利用卡尔曼滤波来解决这个问题。通过计算机仿真研究该滤波器的跟踪效果并与最小二乘法进行比较,估计精度明显高于最小二乘法。同时通过仿真验证该滤波器可以自适应跟踪目标的非稳态运动过程。该方法在工程实践上具有一定应用前景与指导意义。     

平面近场声全息中指数滤波器窗函数设计优化

对基于空间二维傅里叶变换法的平面近场声全息算法中指数滤波器窗函数的合理设置进行了分析和优化。根据优化效果给出一定声源频率条件下,指数滤波器窗函数参数合理设置的建议。同时研究了指数滤波器参数最优设置与声源频率之间的对应关系,并寻找出对应不同声源频率的指数滤波器参数的最优设置。结果表明：相比较窗函数陡度系数的取值变化,滤波器截止波数的取值变化对重建效果影响更加明显;当声源频率在100～1 500 Hz之间变化时,随着声源频率的增加,指数滤波器窗函数的陡度系数的最优值逐渐减小,滤波器截止波数的最优值逐渐增大。     

直角坐标系下的水下被动目标跟踪自适应卡尔曼滤波算法

针对纯方位被动目标跟踪中，直角坐标系下的扩展卡尔曼滤波器容易发散，导致滤波精度很差的情况，文章中提出了一种直角坐标系下自适应卡尔曼滤波算法，对虚拟噪声进行了估计，动态补偿观测模型的线性化误差，削减系统的观测误差，并对其滤波理论及其算法进行了研究和仿真，结果表明，该算法提高了滤波的稳定性、快速性和精确性，优于一般的扩展卡尔曼滤波算法。     

噪声概率快速估计的自适应椒盐噪声消除算法

提出一种可识别噪声概率自动调节滤波窗口的自适应椒盐噪声消除算法。对非理想椒盐噪声污染图像随机区域进行变窗口中值滤波,将结果与滤波前比对获得噪声点数,滤波区域即按此点数排序。然后取每种滤波窗口下的中间三组数据,该数据平均加权获取图像噪声概率初估计,对初估计平均加权即得图像噪声概率。滤波前首先采用阈值法排除明显噪声点,剩余像素中再以离窗口中心像素距离平方的倒数为权值估计中心像素。最后由噪声概率按照T-S模糊规则对不同模型的输出估计值进行融合。实验证明,与传统中值滤波等算法相比,该算法具有噪声自动估计和自适应窗口调节能力,滤波后标准均方差可减少20%以上,速度可提高一倍多。     

A Linear Time-Invariant Filtering Approach for Ultrasonic Transducer Normalization

The increased use of automatic defect detection and characterization systems of the self-learning type has created a demand for means capable of normalizing signals from ultrasonic transducers. Measurements obtained using different measurement setups should be normalized with reference to a standard transducer. It is usually an unfeasible task to optimize characterization procedures for all combinations of measurement parameters that are usually available in a modern complex measurement system. For instance, a change of transducer or only a change in cable length may result in substantial differences in measured data. We propose a linear filtering approach for normalizing ultrasonic pulse-echo measurements as a preprocessing step before presenting the data to a characterization system. The approach requires two data sets: one for the reference transducer and one for the transducer to normalize. We formulate the normalization problem as a general linear approximation problem and derive an optimal linear transformation for an ideal situation with known transducer and noise characteristics. Due to the properties of the optimal linear transformation, a close approximation of this transformation can be implemented using a linear time-invariant filter. We verify by simulations that the filter approximation is valid, and we also examine some properties concerning the accuracy of the estimates obtained using the filter approximation. The filter is obtained using the output error method, one of the standard system identification methods. The proposed method is tested on real ultrasonic data obtained from carbon-fiber—reinforced epoxy composites. The results of experiments with real data, illustrating one of the possible applications, are used to point out some practical considerations that have to be taken into account when implementing the proposed method.     

基于自适应特征融合的多尺度相关滤波跟踪

为了减小目标跟踪中目标变形、光照影响、运动模糊以及目标旋转对跟踪效果的影响,在相关滤波KCF基础上,提出了一种基于自适应特征融合的多尺度相关滤波跟踪算法。首先,提取VGG19网络中conv2-2、conv3-4、conv5-4层的特征以及CN特征,并在conv2-2层加入CN特征;然后,将这3个特征分别代替HOG特征进行滤波学习,得到3幅响应图;进而对3幅响应图进行加权融合预测目标位置。最后,在尺度方面引入多尺度相关滤波器进行尺度的确定。该算法比KCF跟踪算法精确度和成功率分别提高了13.6%和11.8%。与现有的其他优异跟踪算法相比,该算法在应对运动模糊、背景杂乱、目标变形、平面旋转方面更具有较好的跟踪效果。     

广义互相关时延估计声定位算法研究

针对地面目标声定位因信噪比较低而定位精度差的现象,提出了基于广义互相关法的声定位系统,根据平面四元法目标定位计算式,研究了广义互相关算法在实际中的应用。环境噪声、军事目标声和民用目标声频谱范围大多集中,即出现目标声会与背景噪声高度重合的情况,发现一般加窗滤波法在降低噪声的同时亦会将目标声强度大大削弱,而广义互相关时延估计法是通过计算两路信号互相关函数的最大值而求得时延差,其精度高、稳定性好。通过计算机仿真得到在信噪比较低的情况下,加窗滤波因大大削弱目标声强度而造成定位精度较低,而利用广义互相关方法可得到较为精确的声定位坐标。     

包装过程称量信号处理方法研究

目的 为提高包装过程定量称量精度,结合卡尔曼滤波算法和模糊控制原理设计一种称量信号处理方法。方法 定量称量控制系统一般由触摸屏、控制器、称量传感器、变频器等电气设备组成。以传感器信号处理为主要研究对象,提出一种改进卡尔曼滤波算法。采用卡尔曼滤波器实现称量信号中随机噪声的处理。利用模糊控制器来实时监测卡尔曼滤波每次更新后实际方差和理论方差的差值。最后,进行实验研究。结果 实验结果表明,改进卡尔曼滤波的实际性能比较理想,滤波处理前,称量误差最大可以达到2.5%;经滤波处理后,最大称量误差只有0.26%。结论 所述信号处理方法可以有效地降低称量信号噪声,提高称量精度。     

Enhancement of Signal-to-Noise Ratio of Turbulence Measurements by Cross Correlation

A novel method for the measurement of very low turbulence intensities in fluids, based on a dual heat-transfer transducer and a cross correlator, is described. The minimum measurable turbulence intensity is shown to vary with the square root of the minimum detectable cross-correlation coefficient ?. The effects of finite additive noise correlation and finite lateral separation between the transducer halves are studied. Details are given of an instrument measuring the normalized cross-correlation coefficient between two time-dependent signals in the frequency range 2 Hz-300 kHz with an accuracy of ±0.05? ±0.01. The described correlator is particularly suitable for measurements of quasi-stationary processes. A variation of 10 percent in the level of either input signal results in a correlation error of less than 0.6 percent.     

An optimal choice of window width for LOWESS normalization of microarray data

The purpose of normalization in microarray data analysis is to minimize systematic variations in the measured gene expression levels of two co-hybridized mRNA samples so that biological differences can be more easily distinguished. The most commonly and widely used normalization procedure for spotted arrays is probably the intensity dependent and print-tip LOWESS normalization. It is well known that the choices of different parameter values greatly affect the quality of the normalization results, and thus poor quality of the normalization results could be due to the arbitrary choice of the smoothing parameters for LOWESS normalization. In many normalization studies, however, LOWESS has been simply used without rigorous consideration of the parameters. In this article, we propose a bootstrap method to find the optimal window width in print-tip normalization by applying the cross validation technique. We also compare through simulation studies the normalization results by using the proposed method with those by fixing the window width.     

基于核滤波器实时运动目标的抗遮挡再跟踪

相关滤波算法是通过模板与检测目标的相似性来确定目标位置,自从将相关滤波概念用于目标跟踪起便一直受到广泛的关注,而核相关滤波算法的提出更是将这一理念推到了一个新的高度。核相关滤波算法以其高速度、高精度以及高鲁棒性的特点迅速成为研究热点,但核相关滤波算法在抗遮挡性能上有着严重的缺陷。本文针对核相关滤波在抗遮挡性能上的缺陷对此算法进行改进,提出了一种融合Sobel边缘二元模式算法的改进KCF算法,通过Sobel边缘二元模式算法加权融合目标特征,然后计算目标的峰值响应强度旁瓣值比检测目标是否丢失,最后将Kalman算法作为目标遮挡后搜索目标的策略。结果显示,本文方法不仅对抗遮挡有较好的鲁棒性,而且能够满足实时要求,准确地对目标进行再跟踪。     

一种抑制卡尔曼滤波发散的实时数据处理方法

由于水声环境的复杂性和水声信道的时空变特性及水下航行载体的机动性,水声定位系统测量的弹道样点野值较多,平滑性差。介绍了一种野值的自动剔除和卡尔曼滤波递推处理方法,克服了滤波发散。文中选取距离D的倒数作为状态变量,使得1／D是近似线性变化的,此时量测方程的误差也近似是线性的,卡尔曼滤波器的表现是稳定的,并且是渐近无偏的。卡尔曼滤波的递推形式,滤波增益矩阵Kk的离线计算出,Qk和Rk值选取固定植,野值设定门限自动剔除,使滤波器收敛和稳定时间短,实现了对快速目标的跟踪和滤波输出,没有出现发散现象。该方法的特点是实时性好,对快速目标具有良好的跟踪能力,而且能达到工程上应用的精度要求。     

基于近场声全息振速重构的3种滤波方法比较

针对近场声全息反向重构时边缘误差造成的检测精度低的问题,开展基于振速反向重构的近场声全息滤波方法对比研究。以一个高为11.5 cm、半径为4.2 cm的圆柱形发射换能器为研究对象,通过数值仿真分析和实验测量对比二维Harris滤波窗函数、改进后的二维Harris滤波窗函数和WZ滤波窗函数对边缘误差的抑制效果和反向重构声场幅值的误差大小。结果表明,3种滤波窗函数都可以在较短的反向重构距离范围内有效降低边缘误差,利用Harris滤波窗函数进行滤波在反向重构声场的幅值方面误差最小,但边缘误差抑制效果最差;WZ滤波窗函数对反向重构距离的适用性最好,在更大的反向重构距离时,其对边缘误差的抑制效果更好,反向重构误差更小。     

基于混合相关滤波信息融合再检测的目标跟踪算法

为了减小在目标跟踪过程中目标形变和复杂背景变化对跟踪效果的影响,提出一种基于混合相关滤波信息融合再检测的目标跟踪算法。首先,利用相关滤波算法提取到目标的方向梯度直方图HoG特征,利用颜色模板得到目标的颜色特征,计算两个模板的采样得分;其次,再将两者的特征信息用线性组合的形式进行特征信息融合确定目标位置,跟踪过程中,根据设定的阈值条件选择两个模板采样较大的得分再检测目标的位置;最后,输出所有帧目标位置的结果。与其他的算法进行比较,该算法在应对目标形变和背景杂波方面有较好的跟踪效果。