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。     

基于多尺度非线性扩散的超声图像斑点噪声去除算法

针对现有去噪算法可能造成超声图像细节模糊甚至丢失的问题,本文提出基于多尺度非线性扩散（multiscale nonlinear diffusion,MSND）的超声图像去噪模型.该模型结合冗余拉普拉斯塔形数据分解和非线性扩散的优点,利用冗余拉普拉斯塔形数据分解将图像分解为等大小的空间-频率子带,综合各子带的特征得到图像边缘和细节的精细表示,然后根据所得的综合特征指导各子带图像的非线性扩散.实验结果表明本文算法在去除噪声的同时能有效地保留和增强边界与细节.     

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.     

基于滤波反投影算法磁感应成像噪声抑制能力的质量评估

【摘 要】 磁感应成像是一种利用电磁感应原理重构生物组织电导率分布的非接触式电阻抗成像技术。利用一组亥姆霍兹线圈为激励源,能在成像区域内产生均匀分布的激励磁场,从而能够简化成像算法。系统模型由背景物体、异物、亥姆霍兹线圈和20个检测线圈构成。利用滤波反投影算法重构图像,研究该系统下单目标和双目标的图像重构,并且通过并利用相关系数、归一化均方距离和归一化平均绝对距离3个评估参数分析算法对不同噪声的抑制能力。结果表明,当信噪比≥30 dB时,单目标和双目标异物成像效果接近没有噪声的成像结果。     

基于血管内超声图像时空相关性的血流斑点去噪方法

血管内超声成像的优越成像方式使得它越来越广泛地被应用到冠心病的诊断和介入治疗中。但随着超声频率的提高，血流分子回波信号（即血流斑点噪声）也显著增强，这会降低管腔和管壁的对比度，加大医生辨别、测量管腔和斑块几何参数及物理参数的难度。我们提出了一种新颖的去噪方法，它利用血管内超声图像在时间、空间上的相关信息，即组织在时间、空间上的变化比血流小这一事实。实验结果表明该方法能显著地去除斑点噪声，增强管腔和管壁的对比度，更好地帮助医生区别血管壁和周围组织。     

使用有效听觉编码的噪声抑制算法

目的噪声抑制是语音信号处理的重要课题。本文利用有效听觉编码算法处理语音信号,研究其在噪声抑制问题上的效果。方法基于有效听觉编码算法对于语音信号和白噪声信号在编码系数大小分布上具有显著差别的特性,本文提出一种使用有效听觉编码的降噪算法,在有效听觉编码算法获得的编码系数中抑制噪声,适用于含有加性白噪声的语音信号。结果客观评价结果表明本算法能够有效降低语音中的加性白噪声。同时将该算法与其他当前基于短时傅里叶变换的语音降噪算法进行了对比,表明该降噪算法对于信噪比(signal-to-noise ratio,SNR)的提高比当前得到广泛采用的最小均方误差算法(minimum mean-square error,MMSE)、谱减法等性能更好。结论本文提出的噪声抑制算法可以进一步提高当前噪声抑制算法的性能。     

基于快速匹配算法的颈动脉超声图像的运动分析

颈动脉超声图像中的运动信息能够间接地反应颈动脉弹性等状况,结合颈动脉内中膜厚度(CIMT)能够为心脑血管疾病诊断提供定性与定量的依据.我们将改进后的金字塔快速匹配算法(modified block sum pyramid,MBSP)应用于颈动脉超声波图像斑点跟踪获得运动信息.理论和实验结果都表明,改进后的金字塔块匹配算法能有效地减少运动跟踪的运算量,并且有着和改进前的金字塔块匹配算法相同的准确度.运动跟踪结果能够为医生诊断心脑血管疾病起到一定的辅助作用.     

改进的拉普拉斯金字塔多光谱术中引导快速融合算法

本研究利用吲哚菁绿(indocyanine green,ICG)对肿瘤进行标记,通过改进的拉普拉斯金字塔算法对非顶层图像的肿瘤细节进行增强,同时去除顶层图像的非肿瘤信息,从而达到强化与区分肿瘤信息的目的;利用图形处理单元(graphics processing unit,GPU)技术将融合算法进行并行化处理,可以有效提高本研究算法的运算速度,实时显示多光谱融合图像。相对于传统成像方式,本研究提供的成像方法可以实时获取更为全面的肿瘤信息,提高了肿瘤检测的准确性,从而帮助外科医生更好地实施肿瘤切除手术。     

Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering

Image quality is important when evaluating ultrasound images of the carotid for the assessment of the degree of atherosclerotic disease, or when transferring images through a telemedicine channel, and/or in other image processing tasks. The objective of this study was to investigate the usefulness of image quality evaluation based on image quality metrics and visual perception, in ultrasound imaging of the carotid artery after normalization and speckle reduction filtering. Image quality was evaluated based on statistical and texture features, image quality evaluation metrics, and visual perception evaluation made by two experts. These were computed on 80 longitudinal ultrasound images of the carotid bifurcation recorded from two different ultrasound scanners, the HDI ATL-3000 and the HDI ATL-5000 scanner, before (NF) and after (DS) speckle reduction filtering, after normalization (N), and after normalization and speckle reduction filtering (NDS). The results of this study showed that: (1) the normalized speckle reduction, NDS, images were rated visually better on both scanners; (2) the NDS images showed better statistical and texture analysis results on both scanners; (3) better image quality evaluation results were obtained between the original (NF) and normalized (N) images, i.e. NF–N, for both scanners, followed by the NF–DS images for the ATL HDI-5000 scanner and the NF–DS on the HDI ATL-3000 scanner; (4) the ATL HDI-5000 scanner images have considerable higher entropy than the ATL HDI-3000 scanner and thus more information content. However, based on the visual evaluation by the two experts, both scanners were rated similarly. The above findings are also in agreement with the visual perception evaluation, carried out by the two vascular experts. The results of this study showed that ultrasound image normalization and speckle reduction filtering are important preprocessing steps favoring image quality, and should be further investigated.     

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.     

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.     

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.     

高频超声成像中的编码激励研究

本论文实现了基于编码激励的高频超声成像实验系统并对编码激励在实际应用中的性质进行了定量实验研究.以中心频率为10 MHz的眼科B超为实验平台,实现了一个适用于各种二进制编码激励的超声成像系统.系统使用FPGA实现了编码激励实时成像处理,并通过数据采集卡获得射频回波数据.实验表明,采用编码激励可以显著增加系统信噪比,降低发射电压.在单反射面情况下,15 V发射电压的编码激励回波的信噪比与70 V发射电压的单脉冲激励回波的信噪比相当,脉冲压缩的旁瓣水平在-30 dB以下.在超声体模实验中,20 V发射电压的编码激励回波的信噪比水平及穿透深度与60 V发射电压的单脉冲激励系统相当.编码激励实验结果为实现低发射电压的便携式超声诊断系统提供了实验基础.     

高频超声灰阶血流成像系统的研究与实现

目的 探索高频超声的血流成像方法 和血流图像处理方法 ,以实现血流成像系统.方法 根据红细胞对高频超声的散射规律,使用20 MHz超声的机械、线性扫描探头,在一条扫描线上发射多次脉冲,利用脉冲回波相减法提取血流信息.使用模拟血管进行血流成像,并对血流图像进行分析处理.结果 用该高频超声灰阶血流成像系统对模拟血流进行成像,得到的血流图像边缘清晰,并且在对血流图像进行处理后,能很好地滤除血管周围组织产生的伪像,以增强血流信息,从而更好地观察血流形态.结论 在高频超声条件下,使用单脉冲即可提取较强的浅表血流信号,并通过滤除伪像,能实现高频超声条件下的灰阶血流成像.     

超声斑点追踪成像技术在右心功能评估中的研究进展

一直以来,右心室被认为在心功能构成中并未发挥主要作用.随着对右心室结构和功能研究的深入,人们逐渐意识到右心室形态与功能在临床与预后评价中的重要作用,各种用于整体评估右心室功能的成像技术不断得到发展.随着超声心动图技术的发展,斑点追踪成像(STI)技术在右心室功能评估中得到了广泛应用,为不同临床条件下右心室功能评估与研究,以及为与右心室功能相关的心肺疾病诊断与预后评价提供了简单且实用的新方法.对STI技术在右心室功能评估中的原理、方法和意义,以及其在临床应用中的问题作一综述.     

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.     

Shape symmetry analysis of breast tumors on ultrasound images

Shape characteristics of malignant and benign breast tumors are significantly different. In this paper, the reflective symmetry of breast tumor shapes on ultrasound images was investigated. A new reflective symmetry measure (RSML) derived from multiscale local area integral invariant was proposed to quantify the shape symmetry of breast tumor, which could be computed directly from the binary mask image without the shape parameterization in terms of arc length. The performance of several symmetry measures for differentiating malignant and benign breast tumors at varying scales was evaluated and compared by receiver operating characteristic (ROC) analysis. RSML with Gaussian kernel at scale 0.04 (related to the maximal diameter) achieved the highest area under the ROC curve (0.85) on the image data of 168 tumors (104 benign and 64 malignant). The experimental results showed that the reflective symmetry of breast tumor shape was capable of providing potential diagnostic information, which could be characterized quantitatively by RSML with the appropriate scale parameter.     

MP-PCNN超声多普勒血流信号声谱图降噪去斑方法

为有效抑制超声多普勒血流信号声谱图中的背景噪声和多普勒斑点,提出了Matching Pursuit(MP)及单向衰减阈值脉冲耦合神经网络(MP-PCNN)模型。首先将分段的多普勒超声信号进行MP循环分解,分离噪声与信号,然后用单向衰减阈值PCNN模型计算声谱图在各个灰度等级上的点火时刻图并定位斑点,用中值滤波器抑制斑点。通过对各种信噪比的仿真超声多普勒血流信号处理,实验结果表明,MP-PCNN方法可有效地滤除声谱图中的噪声与斑点,并较好地保持边缘与细节信息,在主观及客观性能比较上优于同类降噪去斑方法。