一种新的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。现有的用于斑点噪声抑制的自适应滤波方法,小波软阈值方法及小波域内细节抛弃法在去除噪声的同时,不同程度地丢失了一些图像细节。针对这一问题。本文提出了一种新的结合自适应中值滤波和小波软阈值处理的超声图像斑点噪声抑制方法。对计算机仿真图像及超声图像进行处理的结果表明,本文提出的新方法在有效去除斑点噪声的同时,很好地保留了图像的细节,优于上述的其他方法。     

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.     

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）。     

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.     

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.     

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.     

Enhancing obstetric and gynecology ultrasound images by adaptation of the speckle reducing anisotropic diffusion filter

OBJECTIVE: So far there is no ideal speckle reduction filtering technique that is capable of enhancing and reducing the level of noise in medical ultrasound (US) images, while efficiently responding to medical experts' validation criteria which quite often include a subjective component. This paper presents an interactive tool called evolutionary speckle reducing anisotropic diffusion filter (EVOSRAD) that performs adaptive speckle filtering on ultrasound B-mode still images. The medical expert runs the algorithm interactively, having a permanent control over the output, and guiding the filtering process towards obtaining enhanced images that agree to his/her subjective quality criteria. METHODS AND MATERIAL: We employ an interactive evolutionary algorithm (IGA) to adapt on-line the parameters of a speckle reducing anisotropic diffusion (SRAD) filter. For a given input US image, the algorithm evolves the parameters of the SRAD filter according to subjective criteria of the medical expert who runs the interactive algorithm. The method and its validation are applied to a test bed comprising both real and simulated obstetrics and gynecology (OB/GYN) ultrasound images. RESULTS: The potential of the method is analyzed in comparison to other speckle reduction filters: the original SRAD filter, the anisotropic diffusion, offset and median filters. Results obtained show the good potential of the method on several classes of OB/GYN ultrasound images, as well as on a synthetic image simulating a real fetal US image. Quality criteria for the evaluation and validation of the method include subjective scoring given by the medical expert who runs the interactive method, as well as objective global and local quality criteria. CONCLUSIONS: The method presented allows the medical expert to design its own filters according to the degree of medical expertise as well as to particular and often subjective assessment criteria. A filter is designed for a given class of ultrasound images and for a given medical expert who will later use the respective filter in clinical practice. The process of designing a filter is simple and employs an interactive visualization and scoring stage that does not require image processing knowledge. Results show that filters tailored using the presented method achieve better quality scores than other more generic speckle filtering techniques.     

基于多层小波分解与稳定分布的超声图像散粒噪声抑制新方法

医学超声图像在应用中遇到的一个重要问题是如何消除图像中由于散射现象的相干本质而引起的多径乘性散粒噪声。对数超声图像的二维小波系数服从具有尖峰和拖尾的边缘分布的非高斯分布。α稳定分布可以用来描述这类重拖尾非高斯尖峰脉冲信号和噪声。本研究利用一种散粒噪声模型，通过对对数超声图像的多层小波分解的高频系数的分析与稳定分布建模，提出了一种新的基于闽值的二维小波分解系数的检测分类方法，得到一种基于多层小波分解与稳定分栉模型的超声图像散粒噪声的抑制新方法。仿真结果表明，该方法比传统的基于高斯假设下的阈值去噪方法性能更好。     

A Motion Compounding Technique for Speckle Reduction in Ultrasound Images

The quality of ultrasound images is usually influenced by speckle noise and the temporal decorrelation of the speckle patterns. To reduce the speckle noise, compounding techniques have been widely applied. Partially correlated images scanned on the same subject cross-section are combined to generate a compound image with improved image quality. However, the compounding technique might introduce image blurring if the transducer or the target moves too fast. This blurring effect becomes especially critical when assessing tissue deformation in clinical motion examinations. In this paper, an ultrasound motion compounding system is proposed to improve the quality of ultrasound motion sequences. The proposed motion compounding technique uses a hierarchical adaptive feature weighted motion estimation method to realign the frames before compounding. Each frame is first registered and warped to the reference frame before being compounded to reduce the speckle noise. Experimental results showed that the motion could be assessed accurately and better visualization could be achieved for the compound images, with improved signal-to-noise and contrast-to-noise ratios.     

Wavelet based compression of medical ultrasound images using vector quantization

In this paper, an efficient technique for compression of medical ultrasound (US) images is proposed. The technique is based on wavelet transform of the original image combined with vector quantization (VQ) of high-energy subbands using the LBG algorithm. First, we analyse the statistical behaviour of wavelet coefficients in US images across various subbands and scales. The analysis show that most of the image energy is concentrated in one of the detail subband, either in the vertical detail subband (most of the time) or in the horizontal subband. The other two subbands at each decomposition level contribute negligibly to the total image energy. Then, by exploiting this statistical analysis, a low-complexity image coder is designed, which applies VQ only to the highest energy subband while discarding the other detail subbands at each level of decomposition. The coder is tested on a series of abdominal and uterus greyscale US images. The experimental results indicate that the proposed method clearly outperforms the JPEG2000 (Joint Photographers Expert Group) encoder both qualitatively and quantitatively. For example, without using any entropy coder, the proposed method yields a peak signal to noise ratio gain of 0.2 dB to 1.2 dB over JPEG2000 on medical US images.     

Enhancement of the ultrasound images by modified anisotropic diffusion method

Speckle is a primary factor which degrades the contrast resolution and masks the meaningful texture information present in an ultrasound image. Its presence severely hampers the interpretation and analysis of ultrasound images. When speckle reduction technique is applied for visual enhancement of ultrasound images, it is to be kept in mind that blurring associated with speckle reduction should be less and fine details are properly enhanced. With these points in consideration, the modified speckle reduction anisotropic diffusion (MSRAD) method is proposed in the present study to improve the visual quality of the ultrasound images. In the proposed MSRAD method, the four neighboring pixel template in speckle reduction anisotropic diffusion (SRAD) method of Yu and Acton (IEEE Trans Image Process 11:1260–1270, 2002) have been replaced by a new template of larger number of neighboring pixels to calculate the diffusion term. To enhance visual quality of ultrasound images, nonquadratic regularization (Yu and Yadegar, Proceedings of the IEEE international conference on image processing, 2006) is incorporated with MSRAD method and accordingly changes in parameter settings have been made. The performance of MSRAD method was evaluated using clinical ultrasound images, interpretation by the medical experts and results of MSRAD method by subjective and objective criteria.     

基于Contourlet阈值法的锥形束CT图像去噪研究

目的：将多尺度分析工具之一的Contourlet变换运用到锥形束CT（CBCT）图像去噪领域,并对Contourlet不同阈值去噪方法进行探讨。提出基于Contourlet变换结合半软阈值方法对锥形束CT去噪,并论证去噪效果。方法：利用Contourlet变换的多尺度多方向性以及平移不变性,对低分辨率锥形束CT图像进行拉普拉斯塔形滤波和方向滤波多层分解后得到变换系数,随后对变换系数采用不同阈值方法进行处理,最后逆序反变换得到去噪后图像。通过软阈值和硬阈值方法在Contourlet变换中的应用,提出半软阈值结合Contourlet变换方法对锥形束CT图像去噪。通过对头,胸,盆腔各10例临床锥形束CT图像的去噪,比较三种阈值去噪效果。结果：半软阈值法在胸部和盆腔部锥形束CT图像去噪中比Contourlet硬阈值去噪在PSNR上平均高出1.40 d B和3.11 d B,但在头部锥形束CT图像处理中无优势,而Contourlet软阈值去噪后的锥形束CT图像在消除噪声的同时,信号自身的能量被消弱最多。结论：本文半软阈值法在一定程度上修正了硬,软阈值函数的缺陷,结合Contourlet变换在处理图像几何结构方面的优势,为锥形束CT图像去噪提供了一个新思路。     

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.     

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.     

Entropy-Based Straight Kernel Filter for Echocardiography Image Denoising

A new filter has been proposed with the aim of eliminating speckle noise from 2D echocardiography images. This speckle noise has to be eliminated to avoid the pseudo prediction of the underlying anatomical facts. The proposed filter uses entropy parameter to measure the disorganized occurrence of noise pixel in each row and column and to increase the image visibility. Straight kernels with 3 pixels each are chosen for the filtering process, and the filter is slided over the image to eliminate speckle. The peak signal-to-noise ratio (PSNR) is obtained in the range of 147 dB, and the root mean square error (RMSE) is very low of approximately 0.15. The proposed filter is implemented on 36 echocardiography images, and the filter has the competence to illuminate the actual anatomical facts without degrading the edges.     

基于复小波域非线性扩散的超声图像去噪

超声图像易受斑点噪声的干扰,限制了其在医学诊断中的进一步应用。提出了一种将双树复小波变换(DT-CWT)与非线性扩散相结合的超声图像去噪方法。首先,对图像进行双树复小波分解;然后,高频部分和低频部分分别采用自适应对比度扩散和全变差扩散,最后重构图像。给出了实验结果,并与小波阈值收缩和全变差扩散结合的方法、基于小波和基于多小波的非线性扩散方法的图像去噪效果进行了比较。结果表明,本文提出的方法去噪效果更为优越:不但抑制噪声的能力更强,而且能够更好地保留超声图像原有的边缘和纹理特征。     

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.