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.     

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.     

基于卷积神经网络的超声图像散斑去噪算法

为了提高超声图像质量,解决传统去噪算法在抑制散斑噪声和保留超声图像纹理特征方面的难题,提出一种基于卷积神经网络的超声图像散斑去噪算法DSCNN(De-speckling CNN)。本文提出的算法利用卷积神经网络强大的拟合能力来学习从超声图像到其相应的高质量图像的复杂映射,同时,通过改进损失函数的方式来减少去噪过程中纹理信息的损失和细节的模糊。不同于以往简单地假设超声散斑噪声为乘性噪声,本文利用基于超声图像采集模型和散斑噪声形成模型的模拟超声成像技术为去噪模型生成更贴合真实超声图像的训练数据,解决深度学习方法训练数据匮乏以及在临床上无法获得与超声图像空间配准作为标签的无噪声图像的难题。通过与其他具有代表性的超声图像去噪算法比较,经DSCNN去噪后的超声图像无论在视觉效果还是图像质量评价指标上都取得了更好的结果,其中SSIM达到0.856 9,在文中所有方法中最高。     

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.     

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.     

Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling

Background

Ultrasound imaging is safer than other imaging modalities, because it is noninvasive and nonradiative. Speckle noise degrades the quality of ultrasound images and has negative effects on visual perception and diagnostic operations.

Methods

In this paper, a nonlocal total variation (NLTV) method for ultrasonic speckle reduction is proposed. A spatiogram similarity measurement is introduced for the similarity calculation between image patches. It is based on symmetric Kullback-Leibler (KL) divergence and signal-dependent speckle model for log-compressed ultrasound images. Each patch is regarded as a spatiogram, and the spatial distribution of each bin of the spatiogram is regarded as a weighted Gamma distribution. The similarity between the corresponding bins of the two spatiograms is computed by the symmetric KL divergence. The Split-Bregman fast algorithm is then used to solve the adapted NLTV object function. Kolmogorov-Smirnov (KS) test is performed on synthetic noisy images and real ultrasound images.

Results

We validate our method on synthetic noisy images and clinical ultrasound images. Three measures are adopted for the quantitative evaluation of the despeckling performance: the signal-to-noise ratio (SNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). For synthetic noisy images, when the noise level increases, the proposed algorithm achieves slightly higher SNRS than that of the other two algorithms, and the SSIMS yielded by the proposed algorithm is obviously higher than that of the other two algorithms. For liver, IVUS and 3DUS images, the NIQE values are 8.25, 6.42 and 9.01, all of which are higher than that of the other two algorithms.

Conclusions

The results of the experiments over synthetic and real ultrasound images demonstrate that the proposed method outperforms current state-of-the-art despeckling methods with respect to speckle reduction and tissue texture preservation.

     

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。     

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.     

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.     

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。     

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.     

超声医学图像滤波和对比度增强新方法

较低的对比度和独有的speckle噪声是影响超声医学图像质量的主要原因,本研究利用各向异性扩散滤波,在去除图像中大量噪声的同时,计算滤波过程中图像信息的丢失,从而得到对比度增强模型中的对比度函数,并利用对比度增强模型达到图像对比度增强的目的。实验结果表明,与滤波后的直方图均衡化后结果相比,不仅能够有效地去除图像中的噪声,也能明显提高图像对比度。因此,本文方法是提高超声医学图像质量的一种有效途径。     

Assessment of hybrid speckle reduction algorithms.

A consequence of employing coherent detection methods in medical ultrasound imaging systems is the occurrence of interference effects in the received echo field, which produce the speckle artefact. Speckle can severely degrade the information content of the image, and its efficient removal from ultrasound pulse-echo images is the focus of a number of research projects. Traditionally, the approach towards speckle reduction in pulse-echo images has been based on two classes of technique, either employing some form of spatial/frequency compounding or a data (image) filter. Both approaches have inherent shortcomings, and two alternative techniques are suggested here: 'local frequency diversity' and 'frequency differencing'. These algorithms deterministically identify where speckle occurs, and correct for speckle only within short, localized, corrupted segments of the A-line. This provides the potential for real-time implementation. Simulated and clinical in vivo images have been obtained, and the capabilities of the alternative speckle reduction algorithms are assessed against the more conventional approaches.     

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）。     

An automated diagnostic system of polycystic ovary syndrome based on object growing

Objective

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that seriously affects women's health. The disorder is characterized by the formation of many follicles in the ovary. Currently the predominant diagnosis is to manually count the number of follicles, which may lead to inter-observer and intra-observer variability and low efficiency. A computer-aided PCOS diagnostic system may overcome these problems. However the methods reported in recently published literature are not very effective and often need human interaction. To overcome these problems, we propose an automated PCOS diagnostic system based on ultrasound images.

Methods and materials

The proposed system consists of two major functional blocks: preprocessing phase and follicle identification based on object growing. In the preprocessing phase, speckle noise in the input image is removed by an adaptive morphological filter, then contours of objects are extracted using an enhanced labeled watershed algorithm, and finally the region of interest is automatically selected. The object growing algorithm for follicle identification first computes a cost map to distinguish between the ovary and its external region and assigns each object a cost function based on the cost map. The object growing algorithm initially selects several objects that are likely to be follicles with very high probabilities and dynamically update the set of possible follicles based on their cost functions. The proposed method was applied to 31 real PCOS ultrasound images obtained from patients and its performance was compared with those of three other methods, including level set method, boundary vector field (BVF) method and the fuzzy support vector machine (FSVM) classifier.

Results

Based on the judgment of subject matter experts, the proposed diagnostic system achieved 89.4% recognition rate (RR) and 7.45% misidentification rate (MR) while the RR and MR of the level set method, the BVF method and the FSVM classifier are around 65.3% and 2.11%, 76.1% and 4.53%, and 84.0% and 16.3%, respectively. The proposed diagnostic system also achieved better performance than those reported in recently published literature.

Conclusion

The paper proposed an automated diagnostic system for the PCOS using ultrasound images, which consists of two major functional blocks: preprocessing phase and follicle identification based on object growing. Experimental results showed that the proposed system is very effective in follicle identification for PCOS diagnosis.     

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: pre-processing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice.

Open image in new window

Graphical abstract ?

     

Speckle reduction in ultrasonic images through a maximum likelihood based adaptive filter

Speckle poses serious problems in the interpretation of ultrasound images. It reduces contrast and resolution, making it difficult to identify the presence of abnormalities in B mode images. Using a recently proposed compound probability density function (pdf) for the statistics of the backscattered ultrasonic signals, an adaptive filter for speckle reduction is implemented and tested on B mode images of a tissue mimicking phantom. Results suggest that the adaptive filter based on a maximum likelihood approach improves the ability to classify targets in images while retaining the details in the original unprocessed image.     

乳腺肿瘤超声图像中感兴趣区域的自动检测

由于斑点噪声、伪影以及病灶形状多变的影响,乳腺肿瘤超声图像中肿瘤区域的自动检测以及病灶的边缘提取比较困难,已有的方法主要是由医生先手工提取感兴趣区域(ROI)。本研究提出一种乳腺肿瘤超声图像中感兴趣区域自动检测的方法,选用超声图像的局部纹理、局部灰度共生矩阵以及位置信息作为特征,采用自组织映射神经网络进行分类,自动识别乳腺肿瘤区域。对包含168幅乳腺肿瘤超声图像的数据库进行识别的结果表明:该方法自动识别ROI的准确率达到86.9%,可辅助医生提取肿瘤的实际边缘以及进一步诊断。     

基于各向异性扩散的B超图像去噪

提出了一种基于各向异性扩散方程的B超图像斑点噪声抑制的算法.斑点噪声是由超声成像机制引起的固有噪声形态,它对B超图像的特征提取、识别和分析带来极大困难.特别是对于边缘提取,斑点噪声使得传统的提取算法几乎都无法取得理想的效果.各向异性扩散方程是一种能有效抑制斑点噪声的算法,本文针对原始算法中扩散系数过饱和的问题以及斑点尺度系数选择的不足,提出了改进的方法,从而在抑制斑点噪声的同时保留甚至增强B超图像中的边缘细节信息,为下一步的边缘提取提供了有效保障.