An adaptive squared-distance-weighted interpolation for volume reconstruction in 3D freehand ultrasound

Volume reconstruction is a key procedure in 3D ultrasound imaging. An algorithm named as squared-distance-weighted (SDW) interpolation has been earlier proposed to reduce the blurring effect in the 3D ultrasonic images caused by the conventional distance weighted (DW) interpolation. However, the SDW parameter alpha, which controls the weight distribution, is a constant assigned by operators so that the interpolation effect is invariant for both sharp edges and speckle noises. In this paper, we introduced a new adaptive algorithm based on SDW interpolation for volume reconstruction of 3D freehand ultrasound. In the algorithm, the local statistics of pixels surrounding each voxel grid were used to adaptively adjust the parameter alpha in SDW. The voxel grids with a higher ratio of local variance and mean in their neighbourhoods would have a smaller alpha to make the image details sharper, while the voxel grids locating in regions with a lower ratio of local variance and mean would have a larger alpha to smooth image content in homogeneous regions, where speckle noise is usually observed and damages the image quality. By comparing the simulation results using the SDW and new adaptive algorithm, it was demonstrated that this new algorithm worked well in both edge preservation and speckle reduction.     

基于双边滤波的弱小目标背景抑制

 为解决红外弱小目标检测技术中结构化背景抑制的难题,利用双边滤波集成了图像几何、光度和局部结构相似性等信息并以非迭代、局部操作的优点,提出了一种基于双边滤波的红外弱小目标背景抑制算法,并引入了局部梯度的统计特性来抑制背景细节、增强目标信息,从而达到更好抑制图像中的背景,突出目标图像,提高图像整体对比度、信噪比的目的。实验结果显示,与小波滤波算法比较,该算法对含有弱小目标的复杂背景从主观视觉和数值指标都具有良好抑制效果。     

基于DFT插值的线性约束最小方差宽带自适应阵列

本文提出一种具有频率不变波束图的线性约束最小方差宽带自适应算法。首先给出了具有频率不变波束图的连续线阵的灵敏度函数与离散线列阵加权系数之间的关系，然后给出了使用DFT插值法求解各子带阵列权系数的方法，最后将DFT插值法应用于线性约束最小方差宽带自适应阵列。理论分析及仿真结果表明，该算法可以在实现最小方差波束形成的同时保持波束图基本不随频率变化，且该方法可以降低宽带自适应阵列的运算量。     

极值温差红外图像的自适应双局部增强算法

针对目前红外焦平面成像系统在观察目标、特别是极值温差目标时,各温度段灰度描述不均匀和细节不够的问题,提出了一种自适应红外图像双局部增强算法。详细介绍了通过空间分布和灰度统计特性两个方向实现对极值温差图像自适应增强的方法,该方法首先从红外图像的空间分布特性出发,将图像切割成多个局部图像,然后再从直方图灰度分布出发,将局部图像的直方图进行聚类分段,并对分段直方图均衡增强,最后对生成的每个局部图像增强结果进行线性插值拼接完成增强算法。通过在红外焦平面系统中实验证明了极值温差自适应的红外图像双局部增强算法的可行性,并获得了很好的效果,成像质量有明显提高。     

An adaptive threshold filter for ultrasound signal rejection

The threshold filter is a frequently used technique in ultrasound B-scan to reject the small echoes contributed from backscattering that blur the tissue interface and reduce the image contrast. Note that using the threshold based on one value would simultaneously destroy local waveform features of the reflection echoes with amplitudes larger than threshold value. To resolve this problem, we developed an adaptive threshold filter based on the noise-assisted empirical mode decomposition (EMD). Computer simulations at 7.5 MHz using a single-element transducer with a bandwidth of 60% and a pulselength of 0.5 μs were carried out to explore the feasibility of the algorithm. Image measurements on the carotid artery using a 7.5 MHz, 128 elements, 1D linear array transducer with the same characteristics as those in simulations were used to verify the performance of the algorithm in practice. Compared to the result from the conventional threshold technique, the adaptive threshold filter is able to successfully suppress the smaller backscattering signals without changing the local waveform features of the preserved significant echoes due to refection.     

Volume reconstruction of freehand three-dimensional ultrasound using median filters

Objectives

This paper aims to apply median filters for reducing interpolation error and improving the quality of 3D images in a freehand 3D ultrasound (US) system.

Background and motivation

Freehand 3D US imaging has been playing an important role in obtaining the entire 3D impression of tissues and organs. Reconstructing a sequence of irregularly located 2D US images (B-scans) into a 3D data set is one of the key procedures for visualization and data analysis.

Methods

In this study, we investigated the feasibility of using median filters for the reconstruction of 3D images in a freehand 3D US system. The B-scans were collected using a 7.5 MHz ultrasound probe. Four algorithms including the standard median (SM), Gaussian weighted median (GWM) and two types of distance-weighted median (DWM) filters were proposed to filter noises and compute voxel intensities. Qualitative and quantitative comparisons were made among the results of different methods based on the image set captured in freehand from the forearm of a healthy subject. A leave-one-out approach was used to demonstrate the performance of the median filters for predicting the removed B-scan pixels.

Results

Compared with the voxel nearest-neighbourhood (VNN) and distance-weighted (DW) interpolation methods, the four median filters reduced the interpolation error by 8.0-24.0% and 1.2-21.8%, respectively, when 1/4 to 5 B-scans was removed from the raw B-scan sequence.

Conclusions

In summary, the median filters can improve the quality of volume reconstruction by reducing the interpolation errors and facilitate the following image analyses in clinical applications.     

A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization

In infrared images, detail pixels are easily immerged in large quantity of low-contrast background pixels. According to these characteristics, an adaptive contrast enhancement algorithm based on double plateaus histogram equalization for infrared images was presented in this paper. Traditional double plateaus histogram equalization algorithm used constant threshold and could not change the threshold value in various scenes, so that its practical usage is limited. In the proposed algorithm, the upper and lower threshold value could be calculated by searching local maximum and predicting minimum gray interval and be updated in real time. With the proposed algorithm, the background of infrared image was constrained while the details could also be enhanced. Experimental results proved that the proposed algorithm can effectively enhance the contrast of infrared images, especially the details of infrared images.     

基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法

为了保持高岛分辨率合成孔径雷达(SAR)图像中的纹理结构,提出了一种基于BivaShrink模型的Contourlet 域SAR图像相干斑噪声抑制算法.联合当前层和父层的Contourlet系数,通过计算局部方差一致性范数和区域能量比,自适应地确定方差估计区域的形状和大小.从而对原始图像方差进行最优估计.实验结果表明,算法在噪声的去除和结构信息等细节的保持上均不同程度的优于小波BivaShrink去噪算法和Contourlet 阈值去噪算法,主观效果和数值指标都有较好改进.     

自适应红外目标特征增强算法

利用直方图均衡化和灰度变换增强算法,不能有效增强红外图像目标。鉴于此,在研究红外图像特点的基础上,提出了一种自适应红外目标特征增强算法。该算法先对红外图像进行中值滤波,滤除掉图像中的随机噪声,然后利用直方图分割将红外图像分为目标和背景2部分,通过线性加权叠加抑制背景和增强目标。实验表明,该算法不仅能够根据红外图像中目标的灰度特性自适应地选取直方图分割阈值,而且在去除噪声和增加对比度的同时还抑制了背景,达到了预期的效果。该算法尤其适用于目标和背景像素比例相近时直方图具有局域双峰特征的红外图像中目标的增强。     

改进的最小方差算法在超声成像中的应用

为了提高最小方差超声成像算法的分辨率、对比度以及对噪声的鲁棒性,提出一种改进的最小方差成像算法。该方法首先基于回波信号中期望信号与噪声信号的可分离性将信号划分为期望信号和噪声信号,然后根据最小方差原理,求出加权向量使期望信号功率最小,同时,为了增加算法对噪声的鲁棒性,对信号方向向量增加一对约束条件,进一步提高图像质量。在全发全收和合成孔径模式下对点目标和吸声斑进行仿真,结果表明所提算法在全发全收模式下,-6 dB处分辨率在最小方差基础上提高了1倍左右,在合成孔径模式下,对比度在特征空间最小方差算法基础上提高了8 dB,且远优于传统延时叠加算法。最后通过实验进一步表明改进的最小方差算法图像在分辨率、对比度及对噪声的鲁棒性等方面表现更优,可以有效的改善超声图像的质量。      

一种适用于胸部CR图像自适应增强方法

人体胸部各种组织器官较多,造成胸部的CR(Computed Radiography)图像动态范围大,是所有临床CR图像中最为复杂的一种。一般的图像增强方法只对胸腔中的部分组织起到了增强的效果,而对另一部分组织不会有良好的增强的效果,甚至会出现对比度变差、细节模糊、噪声增加等不理想的结果。提出了基于邻域灰度方差与噪声方差之比的自适应增强方法,可根据胸部CR图像的灰度特性调节增强程度的加权因数K,自适应的增强胸部CR图像边缘细节,丰富图像的细节信息,提高图像的诊断价值。实验证明,该算法处理后的胸部CR图像细节丰富,信噪比高,细节方差和背景方差之比(DV/BV)比通用算法高,增强后的CR图像具有良好的视觉效果,是一种有效的适合胸部CR医学图像的边缘细节增强方法。     

Euclidian distance-weighted smoothing for quantitative MRI: application to intervoxel anisotropy index mapping with DTI

During the computation of intervoxel anisotropy features, the inclusion of both eigenvalues and eigenvectors reduces the effect of noise, but spatial averaging blurs the resulting maps. We propose a new adaptive technique that uses data-dependent weights in the averaging process so that the influence of each neighbor in the local window is proportional to the similarity of characteristics of the neighbor considered to those of the reference central voxel. This likeness criterion is based on the multidimensional Euclidian distance using the entire available multispectral information contained in the diffusion-weighted images. This solution is controlled by a single parameter beta that results from a compromise between edge-preserving and noise-smoothing abilities. This Euclidian distance-weighted technique is a generic solution for filtering noise during parametric reconstruction. It was applied to map anisotropy using an intervoxel lattice index (LI) from experimental images of mouse brain in vivo and achieves noise reduction without distorting small anatomical structures. We also show how to employ in the discrimination scheme the images not used in the estimation of the considered feature.     

Regularized RKHS-Based Subspace Learning for Motor Imagery Classification

Brain–computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject’s signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2–9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm.     

结合松弛中值滤波的高阶彩色图像迭代去噪算法

为去除基于局部平均曲率的彩色图像去噪模型中作为几何特征而保留下来的斑点,提出了一种改进的迭代算法。采用局部平均曲率作为正则项耦合各个颜色通道,在迭代过程中根据局部统计量检测斑点,并引入松弛中值滤波进行斑点抑制。使用不同特征的图像进行仿真实验,并对峰值信噪比的演化进行分析。实验结果表明,改进的算法在有效消除斑点的同时较好地保护了图像结构,并且提高了计算效率。峰值信噪比提高了2.47%,迭代次数减少了93.66%。     

基于自适应扩散模型的单帧红外条纹非均匀性校正算法

针对红外焦平面成像系统存在列向条纹非均匀性的现象,采用了一种基于自适应PM扩散模型的非均匀校正新算法。首先,综合利用图像梯度信息和局部灰度统计信息,自适应计算PM模型的扩散阈值;然后将每列像素的PM模型估计值作为该列像素的期望值;最后采用最陡下降法迭代计算得到每列像元的校正参数,并对结果进行循环校正以提高校正效果。实验结果表明:该算法可以保护图像边缘信息,与同类算法相比,能够更有效地抑制条纹非均匀性,并且能够防止图像产生鬼影。     

Interpolation algorithm for recovering the missing phase values in 3D measurement

In the three-dimensional (3D) phase measurement, some marks are usually adhered to the object in order to make the 3D registration process faster and easier. As covered with marks, local phase data are missing and have to be interpolated later. Considering the phase distribution nearby the marks, a gradient estimate (GE) interpolation algorithm is provided here. This algorithm recovers one pixel's missing phase value with the average of the estimated values which is calculated by gradients in eight directions nearby. Since this algorithm is a local processing, the missing phase values should be interpolated from the edge of the marks to the center. In the computer simulation and the practical experiment, compared with the same-size neighborhood mean (NM) algorithm and the Gerchberg-Saxton (GS) algorithm, this new algorithm achieves very good fit results with the least time. So it can be used as a practical tool for automatic missing phase interpolation.     

基于局部自适应拉升窗的复合图像增强算法

针对含有低亮度低对比度区域的图像,提出基于局部自适应拉升窗(LASW)的复合图像增强算法.通过研究目前一系列基于局部操作的空域图像增强算法,提出全局和局部操作结合的总体思路;首先使用高提升拉普拉斯(Laplacian)反锐化掩模(UM)增强以获得较多的隐藏细节和边缘信息,然后构造局部自适应拉升窗大幅增强低对比度图像细节,同时使用自适应滤波器进行掩模平滑操作;最后根据局部增强结果进行全局修正.仿真实验表明,在绝对误差、图像熵等评价指标下,该算法使低对比度图像尤其当含有低亮度微弱局部信息时,获得了较好的增强效果.     

Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems

Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square （SM-GLMS） algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter （EKF）-based adaptive algorithm and variable gain least mean square （VG-LMS） algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.     

Infrared image detail enhancement based on local adaptive gamma correction

An infrared image detail enhancement method based on local adaptive gamma correction (LAGC) is proposed. The local adaptive gamma values are designed based on the Weber curve to enhance effectively the image details. Subsequently, the active grayscale range of the image processed by LAGC is further extended by using our proposed histogram statistical stretching. The experimental results show that the proposed algorithm could considerably increase the image details and improve the contrast of the entire image. Thus, it has significant potential for practical applications.     

The statistical characteristics of adaptive antenna arrays when processing discrete signals with correlated readouts

The most important statistical characteristics of adaptive antenna arrays are determined. These are the mean value and covariance matrix of the vector of the weighting factors, and the powers of the fluctuations of the weighting factors and of the output signal of the array. It is found that under steady-state operating conditions the fluctuations of the vector of the weighting factors lead to a bias of its mean value for an adaptive antenna array without constraints, but the mean value of the weighting vector of an array with constraints and of an array operating using the criterion of the minimum root mean square error is not biased. It is shown that the effect of fluctuations of the weighting vector on the output signal of an adaptive antenna array may be different and is determined by the value of the correlation coefficient between the input-signal read-outs.Nizhegorod State University. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 37, No. 12, pp. 1532–1545, December, 1994.