基于非参数自适应密度估计理论的医学超声图像去噪方法

在小波变换域中去除图像中的噪声是近年来的研究热点之一。目前在小波域中对加性噪声的去除已经有了许多研究结果,比如Donoho等的处理方法都得到了很好的应用。但是由于超声图像噪声情况的复杂性,其对去噪的方法提出了更高的要求。为了在去除噪声的同时能够更好的保护边缘及有用的细节信息,本研究结合Birg-éMassart等提出的非参数自适应估计理论,提出一种在平稳小波变换域中对超声图像去噪的方法。实验证明,这种基于非参数自适应估计理论的超声图像去噪方法,与Donoho阈值去噪方法相比,去噪效果有所提高。     

基于Bayesian估计的小波自适应阈值方法对图像进行去噪处理的研究

利用Bayesian估计的小波自适应阈值方法对图像进行去噪处理。通过高斯滤波和小波变换的三种方法(传统的硬阈值、传统的软阈值去噪、基于Bayesian估计的自适应阈值去噪)分别同时对加不同标准差σ的Rician噪声信号进行消噪处理,对比验证高斯滤波和传统小波阈值去噪的优劣,以及新的Bayesian估计自适应阈值小波去噪在磁共振成像(magnetic resonance imaging,MRI)图像信号去噪方面的优越性。小波去噪后的信号信噪比比高斯滤波去噪后信号的信噪比高,且均方根误差要低。采用基于Bayesian估计的自适应阈值小波去噪方法比采用的高斯滤波保留了更多有用信号,优化后的氧摄取分数(oxygen extraction fraction,OEF)值有一定程度增大,使结果更接近正电子发射型计算机断层显像(positron emission computed tomography,PET)测量金标准。成功完成信号和噪声分离优化,将一种新的基于Baysian估计的自适应小波阈值去噪应用到了功能核磁共振成像的降噪分析上,取得了不错的效果。     

高重叠率下弹性成像应变估计值的小波去噪研究

目的 高的数据窗重叠率是提高弹性成像轴向分辨率的必要条件,但重叠率的增加会使位移估计的相关误差急剧增长,产生所谓的"蠕虫"噪声.本研究使用小波收缩法去除高重叠率下弹性图像蠕虫噪声.方法 对每一条轴向应变A-line先进行3级离散小波分解,然后根据4种自适应阈值之一使用软阈值函数对每一层小波高频系数进行量化,最后进行小波重构产生去噪后的应变A-line.结果 仿真结果表明提出的技术能有效去除蠕虫噪声,增强弹性图像的信噪比(SNRe)和对比度噪声比(CNRe);与低通滤波相比,使用小波去噪产生的弹性图像更接近于理想弹性图(有更高的相关系数);另外,仿真结果也显示小波去噪应用于应变估计值比应用于位移估计值能获得更好的图像质量参数;弹性体模实验结果也表明该技术能有效改进应变图像性能.结论 小波收缩去噪技术能有效地去除弹性图像的蠕虫噪声,在保持高的轴向分辨率的情况下提高弹性图像的性能.     

基于小波的医学超声图像斑点噪声抑制方法

斑点噪声是超声图像中固有的噪声。本文提出了一种新的去除斑点噪声的方法，这种方法结合中值滤波和多尺度非线性小波软阈值的优点，首先把原网像进行对数转换，然后把对数转换后的图像进行中值滤波处理，从而把转换后的图像分成两部分，对每一部分进行小波分析，假设小波系数服从广义高斯分布（GGD），利用小波系数的统计特性估计出各个部分各个尺度的阈值，最后用软阈值方法对上述两部分分别去噪。实验结果表明，本文提出的方法在有效去除斑点噪声方面，优于中值滤波，维纳滤波和多尺度非线性阈值算法（MSSNT-A）。     

医学设备远程监控图像变换尺度精准去噪方法

目的 为了提高医学设备远程监控图像的去噪效果,针对去噪准确度较差和去噪时间较长的问题,设计一种医学设备远程监控图像变换尺度精准去噪方法。方法 首先建立噪声的变化曲线模型,评估出噪声高等级区域进行针对性的降噪;然后采用小波算法去除图像冗余像素点,引入变换尺度阈值,优化医学设备远程监控图像去噪过程;最后采用去除模糊边缘法分割未成像图片,二次提取模糊图像中的主要像素,实现医学设备远程监控图像变换尺度精准去噪。结果 信息熵值高于21 H,处理过的图像较为清晰,图像信噪比高于21 dB,去噪时间低于4 min。结论 针对医学设备远程监控图像中具有多尺度特征的噪声,采用图像变换尺度精准去噪方法可以有效去除噪声,满足医学领域的实际去噪需求。     

基于小波软阈值滤波的医学图像去噪

目的:为了更好的去除DR医学图像噪声.方法:通过分析其噪声来源,在小波去噪的基础上进行改进.引入方差不变性变换来调整原始图像的噪声模型为高斯噪声模型.图像分解为不同频率的不同子带的小波系数,分别进行不同阈值的滤波.结果:与普通的全局小波去噪方法相比,该方法不但可以保留图像的边缘信息,而且能提高去噪后图像的峰值信噪比.结论:用此方法处理DR图像在噪声去除、细节质量及骨骼锐化等方面比传统的高斯滤波及小波全局阈值滤波等方法效果要好.     

联合主成分分析-小波与整体平均经验模态分解的锋电位去噪方法

多通道微电极阵列记录的锋电位(Spike)十分微弱,极易受干扰,其含噪的特性影响了Spike检出的准确率。针对Spike检测过程中通常存在的独立白噪声、相关噪声与有色噪声,本文结合主成分分析(PCA)、小波分析和自适应时频分析,提出PCA-小波(PCAW)与整体平均经验模态分解(EEMD)联合的去噪新方法(PCWE)。首先,利用PCA提取多通道神经信号通道间的主成分作为相关噪声去除;然后利用小波阈值法对独立白噪声进行去除;最后利用EEMD把噪声分解到各层本质模态函数中,对有色噪声进行去除。仿真结果表明,PCWE使信噪比约提高2.67 dB,标准差约减小0.4μV,显著提高了Spike的检出精确率;实测数据结果表明,PCWE能使信噪比约提高1.33 dB,标准差约减小18.33μV,表现出良好的去噪性能。本文研究结果表明,PCWE可以提高Spike信号的可靠性,或可为神经信号的编码解码提供一种新型有效的锋电位去噪方法。     

非局部主成分分析极大似然估计MRI图像Rician噪声去噪

由于MRI图像中噪声呈Rician分布,直接使用现有针对高斯噪声的去噪方法将引入误差。基于此本研究使用Rician噪声模型改进现有极大似然估计去噪的高斯模型,同时引入非局部主成分分析,在非局部区域选择灰度和纹理均具有较高相似性的像素进行最优复原估计。使用非局部主成分分析不仅克服现有局部性去噪方法模糊边界的缺陷,而且具有更高的图像细节信息复原能力。分别应用所提出的方法、局部极大似然估计去除Rician噪声方法、采用参数修正非局部均值去除Rician噪声方法、无特定噪声模型的全变差方法,对不同噪声等级和不同纹理复杂度的图像进行定性和定量的去噪实验。结果表明,所提出的方法可在保持图像细节和纹理信息的前提下有效去噪,较之现有方法效果更好。     

改进阈值的小波空域相关去噪法在荧光信号处理中的应用

为了去除荧光免疫层析检测中荧光信号的噪声,保留信号的细节信息,提出一种改进阈值的小波空域相关去噪算法。该算法将基于小波变换的空域相关去噪法和软阈值去噪法相结合,根据小波系数相关性的不同和平滑消去阈值法的思想,改进了软阈值去噪法的阈值变量和阈值函数。结果表明,该方法突出了信号边缘,能够有效地去除荧光信号的噪声,去噪后的信号光滑连续,且保留了信号峰的相关细节信息。     

基于GC V算法的背根节神经元放电信号的滤波

为去除背根节神经元放电信号中的噪声,便于进一步分析信号,采用小波滤波法。先将含噪信号采用haar小波进行5层分解,然后在传统小波软阈值滤波的基础上,提出用GCV算法来确定最优阈值,最后进行信号重构。通过matlab仿真实验表明,采用了GCV算法的滤波方法能有效去除神经元放电信号中的噪声,去噪后信号光滑连续好,并且保留了信号峰值的相关细节。     

使用二维小波收缩法去除弹性成像蠕虫噪声

本文研究使用二维小波收缩去噪法去除弹性成像过程中产生的蠕虫噪声。先使用Sym8小波函数对含有蠕虫噪声的应变估计值矩阵进行3级二维离散小波分解,并使用Birg-éMassart算法获取二维小波变换的域值;然后分别使用硬域值函数和软域值函数对各尺度的水平方向、垂直方向、对角方向的高频系数进行量化;最后将第3层低频系数和各层被量化后的高频系数进行二维小波重构产生去噪后的弹性图像。仿真结果显示,提出的技术有效去除了弹性成像的蠕虫噪声,增强了弹性图像的信噪比(SNRe)和对比度噪声比(CNRe),提高了弹性图像与理想弹性图的相关系数(е);与二维低通滤波去噪法相比,使用二维小波收缩法产生的弹性图像有更高的SNRe和CNRe,能更清晰地显示硬物边界。同时,仿真结果也表明该技术对不同应变量的弹性图像的蠕虫噪声均能有效抑制。本研究表明二维小波收缩去噪法能有效去除弹性图像的蠕虫噪声并提高弹性图像性能。     

高重叠率下弹性成像应变估计值的小波去噪研究

目的 高的数据窗重叠率是提高弹性成像轴向分辨率的必要条件,但重叠率的增加会使位移估计的相关误差急剧增长,产生所谓的"蠕虫"噪声.本研究使用小波收缩法去除高重叠率下弹性图像蠕虫噪声.方法 对每一条轴向应变A-line先进行3级离散小波分解,然后根据4种自适应阈值之一使用软阈值函数对每一层小波高频系数进行量化,最后进行小波...     

Electrocardiogram signals de-noising using lifting-based discrete wavelet transform

This paper introduces an effective technique for the denoising of electrocardiogram (ECG) signals corrupted by nonstationary noises. The technique is based on a second generation wavelet transform and level-dependent threshold estimator. Here, wavelet coefficients of ECG signals were obtained with lifting-based wavelet filters. A lifting scheme is used to construct second-generation wavelets and is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method is considered in relation to several measuring parameters, including types of wavelet filters (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Filter(9-7), and Cubic B-splines), thresholding method, and decomposition depth. Three different kinds of noise were considered in this work: muscle artifact noise, electrode motion artifact noise, and white noise. Global performance is evaluated by means of the signal-to-noise ratio and visual inspection. Numerical results comparing the performance of the proposed method with that of nonlinear filtering techniques (median filter) are given. The results demonstrate consistently superior denoising performance of the proposed method over median filtering.     

Selection of mother wavelet and denoising algorithm for analysis of foetal phonocardiographic signals

This paper is aimed at the selection of suitable mother wavelet and denoising algorithm for the analysis of foetal phonocardiographic (fPCG) signals. Fourier based analysing tools have some limitations concerning frequency and time resolutions. Although wavelet transform (WT) overcomes these limitations, it requires proper selection of a mother wavelet and denoising algorithm. In this study a suitable mother wavelet is selected on the basis of properties of different wavelet families and characteristics of the fPCG signals. The universal threshold, minimax threshold and rigorous SURE threshold algorithms along with soft or hard thresholding rule have been compared to denoise these signals. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the fourth order Coiflets wavelet has a better performance for the analysis of fPCG signals when using the rigorous SURE threshold denoising algorithm with soft thresholding rule. The proposed approach is simple and proves to be effective when applied to the selection of suitable mother wavelet and denoising algorithm for the fPCG signals. These denoised signals can be used for the accurate determination of foetal heart rate (FHR) and further diagnostic applications of the foetus.     

Homomorphic wavelet thresholding technique for denoising medical ultrasound images

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.     

基于小波阈值法的脉搏波去噪算法

目的消除可穿戴式脉搏波监测设备在连续测量中由于运动造成的运动伪差,保证设备准确性和稳定性。方法通过选取合适的小波基、小波最大分解层数、阈值函数和阈值方法,对脉搏波信号进行小波阈值处理,提出了一种基于小波阈值法去除脉搏波噪声的算法。并针对在脉搏波信号采集过程中出现的基线漂移、工频干扰和运动伪差,与加窗傅里叶变换去噪后的结果进行对比。结果在信噪比、均方差和平滑度等关键指标上,小波阈值法的效果更优。利用db9小波基对脉搏波信号进行6层小波分解,设置启发式阈值所得到的处理效果最好。结论该算法能够有效抑制工频干扰和运动干扰,使信噪比提高22 dB,均方差接近于0,且平滑度降为原来的11%,实现脉搏波信号采集中干扰的有效去除。     

基于改进小波阈值算法的ECG肌电信号去噪研究

为了解决传统软、硬阈值算法对肌电信号去噪后心电图（ECG）信号幅值降低和存在局部异常尖峰,导致去噪效果较差的问题。通过研究小波阈值算法的去噪原理和优化规则,基于双曲正切函数构造出一种具有连续性、结构简单、灵活性较高的可调阈值函数和改进的分层阈值,并分析得到小波分解含噪ECG信号的最佳小波基函数和分解层数,提出了一种改进的小波阈值算法。将软、硬阈值算法、相关文献中的阈值算法和本文所提改进阈值算法对含有真实肌电信号噪声的ECG信号进行去噪对比研究。实验结果表明：本文改进阈值算法能较好地去除ECG信号中的肌电信号噪声,并能更好地保持ECG信号波形特征,且Pearson相关系数值大于其他阈值算法。定性和定量结果表明,本文所提改进阈值算法对ECG肌电信号噪声具有较好的去噪效果。     

A computer simulation of wavelet noise reduction in computed tomography.

We describe the use of a graphical mathematical spreadsheet programming environment which can be used to simulate the acquisition and reconstruction processes of an x-ray computed tomography (CT) machine. The simulation is used to study the effect of photon counting statistics on the noise in the reconstructed image. Finally we describe and evaluate a novel technique for noise reduction using a nonlinear wavelet filter in which the filter thresholds are calculated individually from the "measured" projection data. This filter is shown to compare favorably to threshold filters based on global estimates of noise variance.     

Digital Radiographic Image Denoising Via Wavelet-Based Hidden Markov Model Estimation

This paper presents a technique for denoising digital radiographic images based upon the wavelet-domain Hidden Markov tree (HMT) model. The method uses the Anscombes transformation to adjust the original image, corrupted by Poisson noise, to a Gaussian noise model. The image is then decomposed in different subbands of frequency and orientation responses using the dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different correction functions were used to shrink the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image. Fifteen radiographic images of extremities along with images of a hand, a line-pair, and contrast–detail phantoms were analyzed. Quantitative and qualitative assessment showed that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction, quality of details, and bone sharpness. In some images, the proposed algorithm introduced some undesirable artifacts near the edges.