Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion

This paper presents a novel approach for speckle reduction and coherence enhancement of ultrasound images based on nonlinear coherent diffusion (NCD) model. The proposed NCD model combines three different models. According to speckle extent and image anisotropy, the NCD model changes progressively from isotropic diffusion through anisotropic coherent diffusion to, finally, mean curvature motion. This structure maximally low-pass filters those parts of the image that correspond to fully developed speckle, while substantially preserving information associated with resolved-object structures. The proposed implementation algorithm utilizes an efficient discretization scheme that allows for real-time implementation on commercial systems. The theory and implementation of the new technique are presented and verified using phantom and clinical ultrasound images. In addition, the results from previous techniques are compared with the new method to demonstrate its performance.     

Speckle reducing anisotropic diffusion

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.     

Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum ${a quad posteriori}$ (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.      

Lesion contrast enhancement in medical ultrasound imaging

Methods for improving the contrast-to-noise ratio (CNR) of low-contrast lesions in medical ultrasound imaging are described. Differences in the frequency spectra and amplitude distributions of the lesion and its surroundings can be used to increase the CNR of the lesion relative to the background. Automated graylevel mapping is used in combination with a contrast-weighted form of frequency-diversity speckle reduction. In clinical studies, the techniques have yielded mean CNR improvements of 3.2 dB above ordinary frequency-diversity imaging and 5.6 dB over sharper conventional images, with no post-processing graylevel mapping     

医学图像增强系统设计

为了改善医学图像的视觉效果,提高图像的清晰度,使之更适合于机器的分析处理以及人的视觉特性,并突出病灶点,为病理学诊断和临床诊断提供可靠依据。设计了一个对医学图像十分具有针对性的图像增强系统。针对CT图像的电子噪声提出了基于修正维纳滤波的小波包去噪算法;针对B型超声图像的散斑噪声提出了基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法;针对医学图像对比度低,边缘信息模糊等特点,提出了基于小波变换的医学图像增强算法。当噪声方差为0.01时,基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法获得的PSNR比经Wiener滤波方法获得的PSNR高出9 dB。系统能快速找到噪声点进行定点去噪,能有效提高医学图像的对比度,增强边缘细节信息,突出病灶点的位置,从而达到较好的处理效果,为医疗工作者观察病症提供更加清晰准确的依据。     

Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.     

基于复小波方向信息的SAR图像斑点噪声抑制

针对一般小波去噪方法在去除合成孔径雷达(Synthetic Aperture Radar-SAR)图像斑点噪声时不能有效保持图像边缘信息的问题,提出结合双密度双树复小波变换（Double-Density Dual Tree Complex Wavelet Transform –DD_DTCWT）方向信息进行边缘检测的SAR图像噪声抑制算法。本文对边缘检测指标进行改进,利用DD_DTCWT方向复小波系数的相对方差作为边缘检测指标,通过相对方差分布密度函数获取阈值处理的自适应门限,由此实现SAR图像的自适应滤波。实验结果表明,本文提出的边缘检测和主方向高频复系数提升方法可以有效保持并增强图像的边缘信息。与SRAD算法和基于DD_DTCWT的双变量收缩函数（Bivariate Shrinkage Function--BSF）算法相比较,本文算法具有更好的边缘保持能力。      

Edge detection in ultrasound imagery using the instantaneous coefficient of variation

The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.     

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.     

一种基于深度学习的自适应医学超声图像去斑方法

针对传统医学超声图像去斑方法的不足,该文提出一种自适应多曝光融合框架和前馈卷积神经网络模型图像去斑方法。首先,制作超声图像训练数据集;然后,提出一种自适应增强因子的多曝光融合框架,增强图像进行有效特征提取;最后,通过网络训练去斑模型并获得去斑后的图像。实验结果表明,该文较已有的方法,能更有效地滤除医学超声图像中的斑点噪声并更多的保留图像细节。     

Contourlet域超声图像自适应降斑算法研究

结合Contourlet系数的结构特点和超声图像相干斑乘性噪声模型,提出了一种新的基于Contourlet变换的斑纹噪声抑制算法.该算法通过计算方差一致性测度(VHM),用局部自适应窗口估计阈值萎缩因子,实现超声图像的降斑处理.实验结果表明,该算法在有效抑制斑纹噪声的同时,更有利于保持图像的边界信息,尤其适用于强噪声背景的超声图像.     

基于生成对抗网络的超声图像超分辨率重建

针对医学超声图像的分辨率低而导致视觉效果差的问题,使用基于神经网络的图像超分辨率(SR)重建方法提升医学超声图像的分辨率。采用针对自然图像超分辨率重建的生成对抗网络(SRGAN)作为基本方法,通过减少2个输入通道和删除1个残差块对该网络的结构进行更改,并且改进网络损失函数,新增模糊处理数据集,使该网络适应医学超声图像所具备的灰度图像、散斑纹理单一等特点,从而重建出放大4倍的边缘清晰没有伪影的医学超声图像。将改进SRGAN与原始SRGAN的结果相比,峰值信噪比(PSNR)和结构相似性(SSIM)分别有1.792 dB和3.907%的提升;与传统双立方插值的结果相比,PSNR和SSIM分别有2.172 dB和8.732%的提升。     

Constrained least squares filtering algorithm for ultrasound image deconvolution

A new medical ultrasound tissue model is considered in this paper, which incorporates random fluctuations of the tissue response and provides more realistic interpretation of the received pulse-echo ultrasound signal. Using this new model, we propose an algorithm for restoration of the degraded ultrasound image. The proposed deconvolution is a modification of the classical regularization technique which combines Wiener filter and the constrained least squares (LS) algorithm for restoration of the ultrasound image. The performance of the algorithm is evaluated based on both the simulated phantom images and real ultrasound radio frequency (RF) data. The results show that the algorithm can provide improved ultrasound imaging performance in terms of the resolution gain. The deconvolved images visually show better resolved tissue structures and reduce speckle, which are confirmed by a medical expert.     

基于各向异性扩散的SAR图像斑点噪声滤波算法

在SAR(Synthetic Aperture Radar)图像噪声抑制处理中,为了有效地保持图像边缘,作者在斑点噪声去除的各向异性扩散模型(SRAD模型)的基础上,提出了一个基于各向异性扩散的SAR图像斑点噪声滤波算法.该算法对应的扩散系数从理论上满足Charbonnier 等人提出的构造扩散系数准则,同时该算法能够通过对边缘直方图上累计百分比和相对信噪比阈值进行调节来得到一系列不同的滤波效果,从而满足不同的应用需求,如绘图、高分辨率或细节丰富的处理结果.实验结果表明,与传统的方法相比,该算法不论从噪声去除能力、边缘和纹理保持能力上,还是从视觉评价效果来看,都具有一定的优越性.     

Despeckling of medical ultrasound images using data and rate adaptive lossy compression

A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: (1) an estimate of the corrupting speckle noise level in the image; (2) the estimated statistics of the noise-free subband coefficients; and (3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality.     

基于局部熵的量子衍生医学超声图像去斑

针对现有医学超声图像去斑方法的不足,该文提出一种基于局部熵的量子衍生医学超声图像去斑新方法。首先,将对数变换后的图像进行双树复小波变换(DTCWT),并对信号与噪声分别建模;然后,提取复小波中子代与父代小波系数的实部,计算其局部熵并进行归一化乘积,结合量子衍生理论得到用来调整信号与噪声出现概率的可调参数;最后,利用改进的双变量收缩函数获得去斑后的图像。通过实验,结果表明该方法与已有方法相比能够更有效地滤除医学超声图像中的斑点噪声并保留细节信息。     

On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering.

In this paper, we focus on the problem of speckle removal by means of anisotropic diffusion and, specifically, on the importance of the correct estimation of the statistics involved. First, we derive an anisotropic diffusion filter that does not depend on a linear approximation of the speckle model assumed, which is the case of a previously reported filter, namely, SRAD. Then, we focus on the problem of estimation of the coefficient of variation of both signal and noise and of noise itself. Our experiments indicate that neighborhoods used for parameter estimation do not need to coincide with those used in the diffusion equations. Then, we show that, as long as the estimates are good enough, the filter proposed here and the SRAD perform fairly closely, a fact that emphasizes the importance of the correct estimation of the coefficients of variation.     

Ultrasound speckle reduction using adaptive wavelet thresholding

Ultrasound is the most widely used biomedical imaging modality for the purpose of diagnosis. It often comes with speckle that results in reduced quality of images by hiding fine details like edges and boundaries, as well as texture information. In this present study, a novel wavelet thresholding technique for despeckling of ultrasound images is proposed. For analysing performance of the method, it is first tested on synthetic (ground truth) images. Speckle noise with distinct noise levels (0.01–0.04) has been added to the synthetic images in order to examine its efficiency at different noise levels. The proposed technique is applied to various orthogonal and biorthogonal wavelet filters. It has been observed that Daubechies 1 gives the best results out of all wavelet filters. The proposed method is further applied on ultrasound images. Performance of the proposed technique has been validated by comparing it with some state-of-the-art techniques. The results have also been validated visually by the expert. Results reveal that the proposed technique outperforms other state-of-the-art techniques in terms of edge preservation and similarities in structures. Thus, the technique is effective in reducing speckle noise in addition to preserving texture information that can be used for further processing.

     

基于数学形态学与自适应的超声医学图像滤波方法的研究

超声医学成像作为主要的医学影像技术之一,因其对人体无伤害、实时、价格便宜和使用方便等优点已广泛应用于临床.然而,在成像过程中形成的特有的图像斑点,使得对比度弱的人体软组织中的正常组织和病变组织不易区分,给临床诊断和医学研究带来不便.针对医学超声图像的特点,在研究了几种常用滤波方法后,提出一种自适应中值滤波和形态滤波结合的新方法,并做了实验验证.实验方法是:首先对所选择的医学超声图像施加瑞利噪声,然后采用中值滤波、自适应中值滤波的方法对被污染的图像进行去噪处理,接下来先采用自适应中值滤波对图像进行预处理,抑制斑点噪声,保留必要细节,再采用数学形态学方法进行二次滤波和增强对比度,进一步改善图像质量.最后从去噪图像和评价指标上对三种滤波去噪方法进行了比较.实验证明,新方法优于其他方法.     

A multiplicative Nakagami speckle reduction algorithm for ultrasound images

Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio.