基于自适应形态滤波的医学超声图像降噪

针对医学超声图像上的斑点噪声,本文提出一种基于自适应形态滤波的降噪方法.首先构造一组检测图像中不同像素值突变的结构因子;再对每个结构因子构造相应的形态滤波结构元;最后对每个像素点邻域进行结构检测,找到该点处最可能存在的突变结构,以相应的结构元完成该点的形态滤波.对不同信噪比的仿真图像和实际图像分别采用本文方法和各向异性扩散滤波,不同尺度传统形态滤波进行了:比较实验,结果表明:采用本方法可将超声图像的信噪比、对比度噪声比和图像优度分别平均提高15%、37%和69%,优于其它方法.     

Ultrasound speckle reduction using modified Gabor filters

B-mode ultrasound images are characterized by speckle artifact, which may make the interpretation of images difficult. One widely used method for ultrasound speckle reduction is the split spectrum processing (SSP), but the use of one-dimensional (1-D), narrow-band filters makes the resultant image experience a significant resolution loss. In order to overcome this critical drawback, we propose a novel method for speckle reduction in ultrasound medical imaging, which uses a bank of wideband 2-D directive filters, based on modified Gabor functions. Each filter is applied to the 2-D radio-frequency (RF) data, resulting in a B-mode image filtered in a given direction. The compounding of the filters outputs give rise to a final image in which speckle is reduced and the structure is enhanced. We have denoted this method as directive filtering (DF). Because the proposed filters have effectively the same bandwidth as the original image, it is possible to avoid the resolution loss caused by the use of narrow-band filters, as with SSP. The tests were carried out with both simulated and real clinical data. Using the signal-to-noise ratio (SNR) to quantify the amount of speckle of the ultrasound images, we have achieved an average SNR enhancement of 2.26 times with simulated data and 1.18 times with real clinical data.     

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.     

保持边缘的SAR图像滤波方法

首先阐明了SAR图像边缘和边缘保持的概念。其次，提出了一种评价平滑图像边缘保持的方法。通过计算发现，常用的标准滤波方法：增强Lee滤波、Kuan滤波、增强Frost滤波和Gamma MAP滤波的边缘保持能力很差。最后发展了一种能保持边缘的SAR图像滤波方法。这种方法是对Han等人去除SAR图像斑点噪声方法的改进，并分析了这种方法能够保持图像边缘的原因。     

用于各向异性扩散滤波的热传导系数构造方法

针对传统各向异性扩散滤波算法难以在噪声环境下有效估计边界像素,本文提出了一种热传导系数构造方法.该方法结合了各向异性扩散和各向同性扩散的优点,将每次迭代运算分解为两步:第一步采用各向同性扩散降低图像噪声,并完成热传导系数的计算;第二步运用各向异性扩散,实现真正的图像滤波.试验证明该方法能够在大尺度加性和乘性混合噪声环境...     

Speckle reduction in optical coherence tomography by adaptive total variation method

An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer’s hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.     

Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue characteristics, the entire image is divided into several levels of speckle-content regions, and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique. Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.     

A motion-based approach to abdominal clutter reduction

In ultrasound images, clutter is a noise artifact most easily observed in anechoic or hypoechoic regions. It appears as diffuse echoes overlying anatomical structures of diagnostic importance, obscuring tissue borders and reducing image contrast. A novel clutter reduction method for abdominal images is proposed, wherein the abdominal wall is displaced during successive-frame image acquisitions. A region of clutter distal to the abdominal wall was observed to move with the abdominal wall, and finite impulse response (FIR) and blind source separation (BSS) motion filters were implemented to reduce this clutter. The proposed clutter reduction method was tested in simulated and phantom data and applied to fundamental and harmonic in vivo bladder and liver images from 2 volunteers. Results show clutter reductions ranging from 0 to 18 dB in FIR-filtered images and 9 to 27 dB in BSS-filtered images. The contrast-to-noise ratio was improved by 21 to 68% and 44 to 108% in FIR- and BSS-filtered images, respectively. Improvements in contrast ranged from 4 to 12 dB. The method shows promise for reducing clutter in other abdominal images.     

Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full-reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full-reference and three no-reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second-best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre-processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies.     

The effect of including myocardial anisotropy in simulated ultrasound images of the heart

We have examined the effect of incorporating tissue anisotropy in simulated ultrasound images of the heart. In simulation studies, the cardiac muscle (myocardium) is usually modeled as a cloud of uncorrelated point scatterers. Although this approach successfully generates a realistic speckle pattern, it fails to reproduce any effects of image anisotropy seen in real ultrasound images. We hypothesize that some of this effect is caused by the varying orientation of anisotropic myocardial structures relative to the ultrasonic beam and that this can be taken into account in simulations by imposing an angle dependent correlation of the scatterer points. Ultrasound images of a porcine heart were obtained in vitro, and the dominating fiber directions were estimated from the insonification angles that gave rise to the highest backscatter intensities. A cylindrical sample of the myocardium was then modeled as a grid of point scatterers correlated in the principal directions of the muscle fibers, as determined experimentally. Ultrasound images of the model were simulated by using a fast k-space based convolution approach, and the results were compared with the in vitro recordings. The simulated images successfully reproduced the insonification dependent through-wall distribution of backscatter intensities in the myocardial sample, as well as a realistic speckle pattern.     

Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming

In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound image. To this end, in this paper, we have used the forward-backward spatial averaging for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The performance of the proposed forward-backward MV (FBMV) beamformer is tested on simulated data obtained using Field II. Data for two closely located point targets surrounded by speckle pattern are simulated showing the higher amplitude resolution of the FBMV beamformer in comparison to the forward-only (F-only) MV beamformers, without the need for diagonal loading. A circular cyst with a diameter of 6 mm and a phantom containing wire targets and two cysts with different diameters of 8 mm and 6 mm are also simulated. The simulations show that the FBMV beamformer, in contrast to the F-only MV, could estimate the background speckle statistics without the need for temporal smoothing, resulting in higher contrast for the FBMV-resulted image in comparison to the MV images. In addition, the effect of steering vector errors is investigated by applying an error of the sound speed estimate to the ultrasound data. The simulations show that the proposed FBMV beamformer presents a satisfactory robustness against data misalignment resulted from steering vector errors, outperforming the regularized F-only MV beamformer. These improvements are achieved without compromising the good resolution of the MV beamformer and resulted from more accurate estimation of the covariance matrix and consequently, the more accurate setting of the MV weights.     

基于多尺度形态学操作的超声图像增强方法

针对医学超声图像低对比度和强噪声给医疗诊断和图像处理所带来的困难，通过基于多尺度形态学操作的方法实现图像增强和噪声抑制的目的．该方法将传统的图像增强概念延伸到数学形态学多尺度空间中，利用多尺度形态学操作提取图像多尺度特征，并通过改变这些特征的强度实现图像局部对比度增强和噪声抑制．实验证明，该方法对超声图像局部对比度增强和噪声抑制是有效的．     

PSF dedicated to estimation of displacement vectors for tissue elasticity imaging with ultrasound

This paper investigates a new approach devoted to displacement vector estimation in ultrasound imaging. The main idea is to adapt the image formation to a given displacement estimation method to increase the precision of the estimation. The displacement is identified as the zero crossing of the phase of the complex cross-correlation between signals extracted from the lateral direction of the ultrasound RF image. For precise displacement estimation, a linearity of the phase slope is needed as well as a high phase slope. Consequently, a particular point spread function (PSF) dedicated to this estimator is designed. This PSF, showing oscillations in the lateral direction, leads to synthesis of lateral RF signals. The estimation is included in a 2-D displacement vector estimation method. The improvement of this approach is evaluated quantitatively by simulation studies. A comparison with a speckle tracking technique is also presented. The lateral oscillations improve both the speckle tracking estimation and our 2-D estimation method. Using our dedicated images, the precision of the estimation is improved by reducing the standard deviation of the lateral displacement error by a factor of 2 for speckle tracking and more than 3 with our method compared to using conventional images. Our method performs 7 times better than speckle tracking. Experimentally, the improvement in the case of a pure lateral translation reaches a factor of 7. Finally, the experimental feasibility of the 2-D displacement vector estimation is demonstrated on data acquired from a Cryogel phantom.     

Quantitative measures of boundary and contrast enhancement in speckle reduction in ultrasonic B-mode images using spatial bessel filters

The spatial Bessel filters are studied for their ability to reduce ultrasonic speckle and enhance the boundaries of regions of interest (ROI) in medical B-mode images. Using the concept of the heterogeneity index, a new parameter is defined to provide a quantitative measure of the edge visibility. It is hypothesized that this edge visibility parameter will be almost equal to unity if the ROI does not contain a boundary and greater than unity when the ROI contains a boundary. Analyses of 2 computer-synthesized images containing speckle and B-mode images of tissue-mimicking phantoms were carried out. The improvement in contrast and enhancement in edge visibility were estimated. In addition, parametric images containing a map of the edge visibility were created that showed clear boundaries. The quantitative measures strongly support the visual perception of contrast and enhanced edge visibility provided by the spatial Bessel filters. Results demonstrate the potential use of Bessel spatial filters in providing speckle reduction along with the ability to enhance the boundaries of regions of interest in B-mode images.     

Use of modulated excitation signals in medical ultrasound. Part II: Design and performance for medical imaging applications

In the first paper, the superiority of linear FM signals was shown in terms of signal-to-noise ratio and robustness to tissue attenuation. This second paper in the series of three papers on the application of coded excitation signals in medical ultrasound presents design methods of linear FM signals and mismatched filters, in order to meet the higher demands on resolution in ultrasound imaging. It is shown that for the small time-bandwidth (TB) products available in ultrasound, the rectangular spectrum approximation is not valid, which reduces the effectiveness of weighting. Additionally, the distant range sidelobes are associated with the ripples of the spectrum amplitude and, thus, cannot be removed by weighting. Ripple reduction is achieved through amplitude or phase predistortion of the transmitted signals. Mismatched filters are designed to efficiently use the available bandwidth and at the same time to be insensitive to the transducer's impulse response. With these techniques, temporal sidelobes are kept below 60 to 100 dB, image contrast is improved by reducing the energy within the sidelobe region, and axial resolution is preserved. The method is evaluated first for resolution performance and axial sidelobes through simulations with the program Field II. A coded excitation ultrasound imaging system based on a commercial scanner and a 4 MHz probe driven by coded sequences is presented and used for the clinical evaluation of the coded excitation/compression scheme. The clinical images show a significant improvement in penetration depth and contrast, while they preserve both axial and lateral resolution. At the maximum acquisition depth of 15 cm, there is an improvement of more than 10 dB in the signal-to-noise ratio of the images. The paper also presents acquired images, using complementary Golay codes, that show the deleterious effects of attenuation on binary codes when processed with a matched filter, also confirmed by presented simulated images.     

Ultrasonic backscatter imaging by shear-wave-induced echo phase encoding of target locations

We present a novel method for ultrasound backscatter image formation wherein lateral resolution of the target is obtained by using traveling shear waves to encode the lateral position of targets in the phase of the received echo. We demonstrate that the phase modulation as a function of shear wavenumber can be expressed in terms of a Fourier transform of the lateral component of the target echogenicity. The inverse transform, obtained by measurements of the phase modulation over a range of shear wave spatial frequencies, yields the lateral scatterer distribution. Range data are recovered from time of flight as in conventional ultrasound, yielding a B-mode-like image. In contrast to conventional ultrasound imaging, where mechanical or electronic focusing is used and lateral resolution is determined by aperture size and wavelength, we demonstrate that lateral resolution using the proposed method is independent of the properties of the aperture. Lateral resolution of the target is achieved using a stationary, unfocused, single-element transducer. We present simulated images of targets of uniform and non-uniform shear modulus. Compounding for speckle reduction is demonstrated. Finally, we demonstrate image formation with an unfocused transducer in gelatin phantoms of uniform shear modulus.     

一种多模板的混合滤波乘性噪声图像去噪算法

提出了一种计算简单的去除图像乘性噪声的自适应混合算法,它通过事先定义一组实现简单的具有不同特性、不同大小窗口的中值和均值滤波器组,根据图像不同区域特征选择不同滤波器进行滤波.该算法能有效地利用空间滤波的特性,且便于硬件实现.实验结果表明,与现有的自适应去噪算法相比,该算法不但计算简单,而且在噪声抑制和细节保留方面综合平衡较好.     

Proposed Framework for Detection of Breast Tumors

Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in the segmentation is the speckle noise. It’s is a multiplicative and negative issue in breast ultrasound imaging, because of this noise, the image resolution and contrast become reduced, which affects the diagnostic value of this imaging modality. As result, speckle noise reduction is very vital for the segmentation process. The framework uses five models such as Generative Adversarial Denoising Network (DGAN-Net), Denoising U-Shaped Net (D-U-NET), Batch Renormalization U-Net (Br-U-NET), Generative Adversarial Network (GAN), and Nonlocal Neutrosophic of Wiener Filtering (NLNWF) for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio (PSNR) for each level of speckle-noise. The five used methods have been compared with classical filters such as Bilateral, Frost, Kuan, and Lee and they proved their efficiency according to PSNR in different levels of noise. The five diverse models are achieved PSNR results for speckle noise at level (0.1, 0.25, 0.5, 0.75), (33.354, 29.415, 27.218, 24.115), (31.424, 28.353, 27.246, 24.244), (32.243, 28.42, 27.744, 24.893), (31.234, 28.212, 26.983, 23.234) and (33.013, 29.491, 28.556, 25.011) for DGAN, Br-U-NET, D-U-NET, GAN and NLNWF respectively. According to the value of PSNR and level of speckle noise, the best image passed for segmentation using U-Net and classification using CNN to detect tumor type. The experiments proved the quality of U-Net and CNN in segmentation and classification respectively, since they achieved 95.11 and 95.13 in segmentation and 95.55 and 95.67 in classification as dice score and accuracy respectively.     

Real-time ultrasound simulation using the GPU

Ultrasound simulators can be used for training ultrasound image acquisition and interpretation. In such simulators, synthetic ultrasound images must be generated in real time. Anatomy can be modeled by computed tomography (CT). Shadows can be calculated by combining reflection coefficients and depth dependent, exponential attenuation. To include speckle, a pre-calculated texture map is typically added. Dynamic objects must be simulated separately. We propose to increase the speckle realism and allow for dynamic objects by using a physical model of the underlying scattering process. The model is based on convolution of the point spread function (PSF) of the ultrasound scanner with a scatterer distribution. The challenge is that the typical field-of-view contains millions of scatterers which must be selected by a virtual probe from an even larger body of scatterers. The main idea of this paper is to select and sample scatterers in parallel on the graphic processing unit (GPU). The method was used to image a cyst phantom and a movable needle. Speckle images were produced in real time (more than 10 frames per second) on a standard GPU. The ultrasound images were visually similar to images calculated by a reference method.     

Speckle reduction in optical coherence tomography images using digital filtering

Speckle noise is a ubiquitous artifact that limits the interpretation of optical coherence tomography images. Here we apply various speckle-reduction digital filters to optical coherence tomography images and compare their performance. Our results indicate that shift-invariant, nonorthogonal wavelet-transform-based filters together with enhanced Lee and adaptive Wiener filters can significantly reduce speckle and increase the signal-to-noise ratio, while preserving strong edges. The speckle reduction capabilities of these filters are also compared with speckle reduction from incoherent angular compounding. Our results suggest that by using these digital filters, the number of individual angles required to attain a certain level of speckle reduction can be decreased.