Nonparametric estimation of ultrasound pulses

An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori knowledge about the structure of RF line echo data and can employ a number of adjacent RF lines from an image. The prime application of the algorithm is to yield a pulse suitable for deconvolution algorithms. This will enable these algorithms to properly take into account the frequency dependence of the attenuation and its variation within a patient and among patients. It is also possible to use the estimated pulse for attenuation estimation, and the consistency of the assumptions underlying the proposed technique is demonstrated by its ability to recover low variance attenuation estimates in the normal liver from in vivo pulse-echo data. Estimates are given for 8 different patients     

Robust estimation of fetal heart rate variability using Doppler ultrasound

This paper presents a new measure of heart rate variability (HRV) that can be estimated using Doppler ultrasound techniques and is robust to variations in the angle of incidence of the ultrasound beam and the measurement noise. This measure employs the multiple signal characterization (MUSIC) algorithm which is a high-resolution method for estimating the frequencies of sinusoidal signals embedded in white noise from short-duration measurements. We show that the product of the square-root of the estimated signal-to-noise ratio (SNR) and the mean-square error of the frequency estimates is independent of the noise level in the signal. Since varying angles of incidence effectively changes the input SNR, this measure of HRV is robust to the input noise as well as the angle of incidence. This paper includes the results of analyzing synthetic and real Doppler ultrasound data that demonstrates the usefulness of the new measure in HRV analysis.     

Robust minimum-distance estimation using the 3-parameter Weibulldistribution

Maximum-likelihood and minimum-distance estimates were compared for the three-parameter Weibull distribution. Six estimation techniques were developed by using combinations of maximum-likelihood and minimum-distance estimation. The minimum-distance estimates were made using both the Anderson-Darling and Cramer-Von Mises goodness-of-fit statistics. The estimators were tested by Monte Carlo simulation. For each set of parameters and sample size, 1000 data sets were generated and evaluated. Five evaluation criteria were calculated; they measured both the precision of estimating the population parameters and the discrepancy between the estimated and population Cdfs. The robustness of the estimation techniques was tested by fitting Weibull Cdfs to data from other distributions. Whether the data were Weibull or generated from other distributions, minimum-distance estimation using the Anderson-Darling goodness-of-fit statistic on the location parameter and maximum likelihood on the shape and scale parameters was the best or close to the best estimation technique     

Robust estimation using the Robbins-Monro stochastic approximation algorithm

The problem of minmax estimation of a location parameter introduced by Huber is considered. It is shown that under general conditions there exists a solution which is a form of the Robbins-Monro stochastic approximation algorithm. This generalizes earlier work by Martin and Masreliez who have given stochastic approximation (SA)-estimate solutions for two particular cases. As with theM-estimate solutions given by Huber, the SA solutions are completely determined by the probability distribution function with least Fisher information in the distribution set used to model the observation errors.     

Robust motion estimation for night-shooting videos using dual-accumulated constraint warping

This paper introduces a novel concept of dual-accumulated constraint projection warping, as a robust and efficient motion estimation solution for night video stabilization. Small imaging-sensors used in compact hand-held cameras become very prone to noise and blur under low illumination condition. Restricted lighting results in dark boundaries and degrades textural information of the frame. Presence of these combined textural artifacts makes night-shooting a hard problem for accurate motion estimation. At poor lighting, local intensity variations result in failure of inter-frame feature or block matching correspondence. In the proposed technique, use of projection ensures accuracy under local perturbations, noise and blur conditions, while dual-accumulation eliminates the effect of dark-regions adding robustness to night-shooting condition. Efficiency of the proposed algorithm over the existing motion estimation techniques is tested and verified over different categories of night shooting videos. In addition to night video stabilization the proposed scheme also performs well under normal illumination.     

Adaptive shrinkage de-noising using neighbourhood characteristic

A new adaptive shrinkage de-noising algorithm for images based on the neighbourhood characteristics is presented. This algorithm determines the shrinkage thresholds according to the neighbouring coefficients and the noise level. The experimental results show a great improvement with respect to the standard wavelet shrinkage algorithms     

Estimation of pulses in ultrasound B-scan images

It is shown, based on an expression for the received pressure field in pulsed medical ultrasound systems, that a common one-dimensional pulse can be estimated from individual A-lines. An autoregressive moving average (ARMA) model is suggested for the pulse, and an estimator based on the prediction error method is derived. The estimator is used on a segment of an A-line, assuming that the pulse does not change significantly inside the segment. Several examples of the use of the estimator on synthetic data measured from a tissue phantom and in vitro data measured from a calf's liver are given. They show that a pulse can be estimated even at moderate signal-to-noise ratios     

Blood velocity estimation using ultrasound and spectral iterative adaptive approaches

This paper proposes two novel iterative data-adaptive spectral estimation techniques for blood velocity estimation using medical ultrasound scanners. The techniques make no assumption on the sampling pattern of the emissions or the depth samples, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions. Furthermore, the techniques are shown, using both simplified and more realistic Field II simulations as well as in vivo data, to outperform current state-of-the-art techniques, allowing for accurate estimation of the blood velocity spectrum using only 30% of the transmissions, thereby allowing for the examination of two separate vessel regions while retaining an adequate updating rate of the B-mode images. In addition, the proposed methods also allow for more flexible transmission patterns, as well as exhibit fewer spectral artifacts as compared to earlier techniques.     

Brain shift estimation in image-guided neurosurgery using 3-D ultrasound

Intraoperative brain deformation is one of the most important causes affecting the overall accuracy of image-guided neurosurgical procedures. One option for correcting for this deformation is to acquire three-dimensional (3-D) ultrasound data during the operation and use this data to update the information provided by the preoperatively acquired MR data. For 12 patients 3-D ultrasound images have been reconstructed from freehand sweeps acquired during neurosurgical procedures. Ultrasound data acquired prior to and after opening the dura, but prior to surgery, have been quantitatively compared to the preoperatively acquired MR data to estimate the rigid component of brain shift at the first stages of surgery. Prior to opening the dura the average brain shift measured was 3.0 mm parallel to the direction of gravity, with a maximum of 7.5 mm, and 3.9 mm perpendicular to the direction of gravity, with a maximum of 8.2 mm. After opening the dura the shift increased on average 0.2 mm parallel to the direction of gravity and 1.4 mm perpendicular to the direction of gravity. Brain shift can be detected by acquiring 3-D ultrasound data during image-guided neurosurgery. Therefore, it can be used as a basis for correcting image data and preoperative planning for intraoperative deformations.     

Noninvasive estimation of tissue temperature response to heatingfields using diagnostic ultrasound

A noninvasive technique for monitoring tissue temperature changes due to heating fields using diagnostic ultrasound is described. The approach is based on the discrete scattering model used in the tissue characterization literature and the observation that most biological tissues are semi-regular scattering lattices. It has been demonstrated by many researchers and verified by the authors that the spectrum of the backscattered radio frequency (RF) signal collected with a diagnostic ultrasound transducer from a semi-regular tissue sample exhibits harmonically related resonances at frequencies determined by the average spacing between scatterers along a segment of the A-line. It is shown theoretically and demonstrated experimentally (for phantom, in vitro, and in vivo media) that these resonances change with changes in the tissue temperature within the processing window. In fact, changes in the resonances (Δf) are linearly proportional to changes in the temperature (ΔT), with the proportionality constant being determined by changes in the speed of sound with temperature and the linear coefficient of thermal expansion of the tissue. Autoregressive (AR) model-based methods aid in the estimation of Δf. It should be emphasized that this new technique is not a time of flight velocimetric one, so it represents a departure from previously used ultrasonic methods for tissue temperature estimation     

Robust ego-motion estimation and 3-D model refinement using surface parallax.

We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse depth map using parametric surface parallax models and brightness derivatives extracted from an image pair. Given a coarse depth map acquired by a range-finder or extracted from a digital elevation map (DEM), ego-motion is estimated by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth estimate, motion of the three-dimensional (3-D) points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field, and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. The parallax magnitude is estimated using a constant parallax model (CPM) which assumes a smooth parallax field and a depth based parallax model (DBPM), which models the parallax magnitude using the given depth map. We obtain confidence measures for determining the accuracy of the estimated depth values which are used to remove regions with potentially incorrect depth estimates for robustly estimating ego-motion in subsequent iterations. Experimental results using both synthetic and real data (both indoor and outdoor sequences) illustrate the effectiveness of the proposed algorithm.     

Robust estimation via stochastic approximation

It has been found that robust estimation of parameters may be obtained via recursive Robbins-Monro-type stochastic approximation (SA) algorithms. For the simple problem of estimating location, appropriate choices for the nonlinear transformation and gain constant of the algorithm lead to an asymptotically min-max robust estimator with respect to a familymathcal{F} (y_p,p)of symmetrical distributions having the same masspoutside[-y_p,y_p], 0 < p < 1. This estimator, referred to as thep-point estimator (PPE), has the additional striking property that the asymptotic variance is constant over the familymathcal{F}(Y_p,p). The PPE is also efficiency robust in large samples. Monte Carlo results indicate that small sample robustness may be obtained using both one-stage and two-stage procedures. The good small-sample results are obtained in the one-stage procedure by using an adaptive gain sequence, which is intuitively appealing as well as theoretically justifiable. Some possible extension of the SA approach are given for the problem of estimating a vector parameter. In addition, some aspects of the relationship between SA-type estimators and Huber'sM-estimators are given.     

Robust time delay estimation of bioelectric signals using least absolute deviation neural network

The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum L1-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of L1-norm model superior to Lp-norm (p > 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC).     

Robust estimation of the Birnbaum-Saunders distribution

The Birnbaum-Saunders distribution is prevalent in the engineering sciences as an effective means of modeling fatigue life. In practice however, there is no guarantee that the collected data follow such a model. Consequently, this paper considers the robust estimation of the parameters and quantiles of this distribution. Our robust estimation technique is based on OBRE (optimal bias-robust estimator) and assigns a weight to each observation and gives estimates of the parameters and quantiles based on data which are well modeled by the distribution. Thus, observations which are not consistent with the proposed distribution can be identified and the validity of the model assessed. An `application to aluminum fatigue data' and `simulation results' provide strong evidence in support of OBRE. OBRE performs more than adequately for practical purposes. Furthermore, efficiency in many ways becomes a nonissue as we move away from the model. We must give up some degree of efficiency to gain robustness, and OBRE provides a powerful method of doing so. The simulation study shows that compromises can be made which are effective in both regards. Since statistical-confidence intervals can be calculated for OBRE, robust statistical-confidence interval estimates for the critical time of the hazard rate can also be obtained. These techniques are fundamental in describing, analyzing, and comparing fatigue data so that engineers can achieve the desired reliability on a rational basis and at the same time avoid serious consequences stemming from incorrect inference     

相机自运动参数的鲁棒性估计

相机自运动估计是视觉导航中的关键技术之一,主要是通过分析相机在不同位置拍摄到的场景图像来获取相机的运动信息.从数学上讲,相机自运动估计已经形成了完备的理论基础,但是由于图像中包含大量的噪声,会使算法的性能大幅度降低,因此,如何提高自运动估计的鲁棒性是当前面临的主要问题.主要研究了基于匹配点对的自运动估计的鲁棒性问题,其核心思想是:同时利用多种算法进行自运动参数估计,从中选择最优的估计结果以提高算法性能.首先利用SIFT特征提出两幅图像中的匹配点对,然后采用一种匹配点对选取策略减小匹配点对的错误率.利用多种方法对基本矩阵进行估计,依据成像约束关系从中选择最优估计,以获得最佳估计结果.最后利用仿真数据和实验图像对算法进行验证,实验结果表明了算法的有效性.     

Robust estimation of structured covariance matrices

In the context of the narrowband array processing problem, robust methods for accurately estimating the spatial correlation matrix using a priori information about the matrix structure are developed. By minimizing the worse case asymptotic variance, robust, structured, maximum-likelihood-type estimates of the spatial correlation matrix in the presence of noises with probability density functions in the ∈-contamination and Kolmogorov classes are obtained. These estimates are robust against variations in the noise's amplitude distribution. The Kolmogorov class is demonstrated to be the natural class to use for array processing applications, and a technique is developed to determine exactly the size of this class. Performance of bearing estimation algorithms improves substantially when the robust estimates are used, especially when nonGaussian noise is present. A parametric structured estimate of the spatial correlation matrix that allows direct estimation of the arrival angles is also demonstrated     

Robust methods for model order estimation

Model order estimation is a subject in time series analysis that deals with fitting a parametric model to a vector of observations. This paper focuses on several model order estimators known in the literature and examines their performance under small deviations of the probability distribution of the noise with respect to a nominal distribution assumed in the model. It is demonstrated that the standard estimators suffer from high sensitivity to deviations from the nominal distribution, and a drastic performance degradation is experienced. To overcome this problem, robust estimators that are insensitive to small deviations from the nominal distribution are developed. These estimators are based on a composition between model order estimation methods and robust statistical inference techniques known in the literature. In addition, a new estimator based on a locally best test for weak signals is presented both in nonrobust and robust versions. The proposed robust model order estimators are developed on a heuristic basis, and there is no claim of optimality, but experimental results indicate that they provide significant improvement over the standard estimators     

Robust parameter estimation for mixture model

In pattern recognition, when the ratio of the number of training samples to the dimensionality is small, parameter estimates become highly variable, causing the deterioration of classification performance. This problem has become more prevalent in remote sensing with the emergence of a new generation of sensors with as many as several hundred spectral bands. While the new sensor technology provides higher spectral and spatial resolution, enabling a greater number of spectrally separable classes to be identified, the needed labeled samples for designing the classifier remain difficult and expensive to acquire. Better parameter estimates can be obtained by exploiting a large number of unlabeled samples in addition to training samples, using the expectation maximization algorithm under the mixture model. However, the estimation method is sensitive to the presence of statistical outliers. In remote sensing data, miscellaneous classes with few samples are often difficult to identify and may constitute statistical outliers. Therefore, the authors propose to use a robust parameter-estimation method for the mixture model. The proposed method assigns full weight to training samples, but automatically gives reduced weight to unlabeled samples. Experimental results show that the robust method prevents performance deterioration due to statistical outliers in the data as compared to the estimates obtained from the direct EM approach     

Robust weighted likelihood estimation of exponential parameters

The problem of estimating the parameter of an exponential distribution when a proportion of the observations are outliers is quite important to reliability applications. The method of weighted likelihood is applied to this problem, and a robust estimator of the exponential parameter is proposed. Interestingly, the proposed estimator is an /spl alpha/-trimmed mean type estimator. The large-sample robustness properties of the new estimator are examined. Further, a Monte Carlo simulation study is conducted showing that the proposed estimator is, under a wide range of contaminated exponential models, more efficient than the usual maximum likelihood estimator in the sense of having a smaller risk, a measure combining bias & variability. An application of the method to a data set on the failure times of throttles is presented.