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）。     

基于树状小波的超声医学图像阈值函数降噪

针对超声医学图像中存在特有的斑点噪声,利用树状小波分解比传统小波分解精度高的特点,将超声医学图像进行树状小波分解,然后分别采用硬阈值、软阈值和半软阈值函数三种方法进行降噪处理.结果表明半软阈值函数方法是较优阈值函数方法,可以有效地降低原图像的斑点噪声并保留图像细节.     

基于小波变换的血管内超声图像血流斑点噪声抑制研究

血管内超声成像已经越来越广泛地应用到冠心病的诊断和介入治疗中.为了提高图像分辨率必须增加超声频率,使得血流斑点噪声也显著增强,降低了管腔和管壁的对比度,增加了识别管壁与周围组织的难度,给病情的诊断和治疗带来了不便.本研究结合小波变换域软阈值滤波法和半软阈值滤波法,并设计了一种局部阈值来实现血流斑点噪声抑制.实验结果表明该方法在抑制斑点噪声的同时保留了图像的边缘,增强了管腔和管壁的哪对比度,有助于识别管壁和周围组织.     

基于变分法的超声图像斑点噪声自适应滤波算法

各向异性扩散模型在去除超声图像斑点噪声时不能有效保护图像细节,针对上述问题本文提出基于变分法的自适应最小能量去噪模型.首先直接将由微分方程表示的各向异性扩散模型转化为最小能量变分模型;然后引入欧拉弹性能量模型,在去除噪声的同时有效地保护和增强图像细节.同时为了解决数值求解过程中出现的迭代次数与迭代步长的矛盾,本文还提出迭代停止准则和自适应变步长去噪算法.仿真和真实超声图像的实验结果表明基于变分法的超声图像斑点噪声自适应滤波算法在去噪的同时能够很好地保护细节信息,而且能有效地减少迭代次数.     

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。     

眼科高频超声成像中斑点噪声抑制算法的研究

目的 斑点噪声是超声图像中存在的固有问题,而在眼科高频超声这种更为精细的超声检查中,有效地抑制斑点噪声能提高图像的质量,有助于临床医生对病情的判别.方法 提出了一种新的基于拉普拉斯（Laplacian）金字塔的多尺度斑点去噪方法.采用Laplacian金字塔,从斑点噪声中分离出临床图像特征,根据每层子带图像不同尺度及特点,从小尺度到大尺度,首先采用改进后的八方向各向异性斑点去噪（SRAD）去除图像斑点,然后增强图像的边缘、细节及对比度等方面.该方法与传统的SRAD滤波及相干增强滤波（CEDIF）进行对比,采用等效视数及算法的时间耗费对实验结果进行量化评估.结果 与传统SRAD滤波及CEDIF滤波方法相比,基于Laplacian金字塔的多尺度各向异性斑点去噪方法均高于前两种方法（1.172 3 vs 1.122 3、0.929 3及0.864 0 vs 1.396 0、1.468 3）.结论 本研究提出的基于Laplacian金字塔的多尺度各向异性斑点去噪方法在更有效地去除图像斑点噪声的同时,能很好地保存图像边缘及图像细节等.     

基于比率距离的自适应超声图像去噪方法

针对超声图像噪声的瑞利分布特性,使用一种新的自适应超声图像去噪方法,改进固定窗口包含边缘时无法做到沿边缘方向滤波的不足。采用可自由伸缩的自适应滤波窗口,首先针对瑞利分布的噪声引入比率距离,得到超声图像像素间的相似度距离,然后考虑像素的邻域图像块均值,解决相似度距离之间比较的问题,最后像素根据新的相似度距离进行八方向伸展,得到不规则形状的滤波窗口进行去噪。用仿真超声图像和临床超声图像进行实验,图像评价指标结果表明该算法优于经典算法,更适用于去除超声图像的斑点噪声,在去除噪声的同时能够较好地保留细节边缘。     

基于小波统计模型的医学超声图像去噪方法研究

超声图像中固有的斑点噪声严重降低图像的可解译程度,影响了后续的图像分析和诊断.因此,抑制相干斑噪声一直是医学超声图像预处理中一个关键性问题.本研究通过对含斑图像做对数变换和冗余小波分解,提出了一种基于Bayesian估计的小波域局部自适应性去斑算法.将斑点噪声和有用信号的小波系数分别建模为瑞利分布和拉普拉斯分布,利用最大后验概率(MAP)准则得到了一种解析的Bayesian估计表达式;进一步通过邻域窗口估计模型参数,使算法具有局部自适应性.实验仿真表明,该算法简单有效,在滤除超声图像斑点噪声的同时,较好地保持了图像的细节特征,其性能优于空间域滤波和传统的小波去噪算法.     

基于自适应邻域的超声图像加权中值滤波

提出了一种自适应邻域中值滤波算法，用于医学超声内窥镜图像的噪声滤除。该方法以图像象素邻域的灰度方差为阈值，进行保持与修复窗口的自适应改变，在有效抑制Speckle噪声的同时，较好保留了图像的细节信息。对本算法与Loupas提出的加权中值滤波算法进行了比较，指出本算法在一定程度上克服了加权中值滤波器的不足并保留了它的优点，对超声内窥镜图像的滤噪有较好的效果。     

基于简化的PCNN在超声乳腺癌图像去噪方面的应用

针对乳腺癌超声图像中斑点对诊断的影响,提出一种基于简化的脉冲耦合神经网络(simplified pulse-coupled neuralNetwork,SPCNN)的去噪新方法,并将此方法应用于乳腺癌超声图像滤波。首先利用简化的PCNN定位极端脉冲噪声点并利用中值滤波滤除椒盐噪声,然后利用PCNN赋时矩阵采用分类滤波自适应调节灰度值滤除高斯噪声。用实验图像验证了方法的有效性,然后将此方法应用于乳腺癌的超声图像中进行滤波,实验结果证实该方法对混合噪声在滤波效果和保护细节方面具有优势,对乳腺癌的超声图像能较好地滤除噪声,同时保证了细节,结合医学诊断证实了该方法的有效性。     

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.     

Speckle reduction in ultrasound medical images using adaptive filter based on second order statistics

This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.     

Speckle reduction in ultrasound medical images using adaptive filter based on second order statistics

This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.     

Robust non-homomorphic approach for speckle reduction in medical ultrasound images

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.     

Novel Genetic-Neuro-Fuzzy Filter for Speckle Reduction from Sonography Images

Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations.The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filters output to the edge detection algorithm and observing its ability to detect edge pixels.Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.     

冠状动脉造影图像降噪处理的3种方法比较

目的 对冠状动脉造影图像降噪处理的3种方法进行比较。方法 将冠状动脉造影图像数字化并输入计算机，然后再用中值滤波法，自适应滤波法和基于小波变换的降噪处理3种方法分别处理同一图像，比较效果。结果 成功地运用了3种方法对冠状动脉造影图像进行降噪处理，图像质量均有所提高。结论 自适应降噪处理和基于小波系数的降噪处理结果较好，但自适应降噪处理的速度要快。     

Wavelet-based statistical approach for speckle reduction in medical ultrasound images

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Kσ/σ_x, where σ and σ_x were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.