An investigation of simulated umbilical artery Doppler waveforms. III. The effect of noise reduction algorithms on the maximum frequency envelope and on the pulsatility index.

The effect of two noise reduction algorithms on the accuracy of estimation of the maximum frequency envelope and pulsatility index (PI) of simulated umbilical artery Doppler waveforms was investigated. The algorithms were: first, smoothing of the envelope from unfiltered Doppler spectra using a double window modified trimmed mean (DWMTM) filter and second, speckle and noise reduction of the Doppler spectrum using an image processing method. The test population consisted of waveforms were the degree of beam-vessel misalignment had been varied. The accuracy of estimation of the maximum frequency envelope and the PI was calculated by comparing each set of waveforms with the gold-standard maximum frequency envelope from the ensemble averaged waveform obtained with no misalignment. Speckle reduction gave rise to PI values that were low by approximately 0.1 (3%-4%). When there was no background noise present the improvements in envelope estimation were factors of 1.27 and 1.24, respectively, for the DWMTM method and the spectral filter, whereas the factors were 1.56 and 2.07 when background noise was present. For estimation of PI the DWMTM filter was superior. For no background noise the DWMTM filter gave a factor of 3.36 improvement whereas there was no improvement with the spectral filter. When background noise was present the factors for improvement in PI estimation were 2.39 and 4.16.     

SRBF: Speckle Reducing Bilateral Filtering

Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). (E-mail: balocco.simone@gmail.com)     

Speckle reduction with edge preservation in medical ultrasonic images using a homogeneous region growing mean filter (HRGMF)

In this paper, we propose a new post formation adaptive image filtering technique, to be called the homogeneous region growing mean filter, in order to reduce speckle noise with edge preservation in medical ultrasonic images. First, to find a proper seed region, an initially assumed seed region, which is larger than the average speckle size around a given filtering point, is successively contracted according to a certain local homogeneity criterion. Once the seed region is determined, the next step is to grow the homogeneous region successively based on some measures of local homogeneity and similarity of the neighboring region. The output of the proposed filter for each filtering point is obtained from the arithmetic mean of the grown locally homogeneous region. Several simulation results are presented to illustrate the performance of the proposed filter. They show that the proposed technique effectively smoothes ultrasonic speckle and completely suppresses isolated impulsive noise over the entire texture in addition to preserving the edge information.     

Speckle reduction in ultrasound images using nonisotropic adaptive filtering

In this article, a speckle reduction approach for ultrasound imaging that preserves important features such as edges, corners and point targets is presented. Speckle reduction is an important problem in coherent imaging, such as ultrasound imaging or synthetic aperture radar, and many speckle reduction algorithms have been developed. Speckle is a non-additive and non-white process and the reduction of speckle without blurring sharp features is known to be difficult. The new speckle reduction algorithm presented in this article utilizes a nonhomogeneous filter that adapts to the proximity and direction of the nearest important features. To remove speckle without blurring important features, the location and direction of edges in the image are estimated. Then for each pixel in the image, the distance and angle to the nearest edge are efficiently computed by a two-pass algorithm and stored in distance and angle maps. Finally for each pixel, an adaptive directional filter aligned to the nearest edge is applied. The shape and orientation of the adaptive filter are determined from the distance and angle maps. The new speckle reduction algorithm is tested with both synthesized and real ultrasound images. The performance of the new algorithm is also compared with those of other speckle reduction approaches and it is shown that the new algorithm performs favorably in reducing speckle without blurring important features.     

Enhancement of SAR images using curvelet with controlled shrinking technique

The speckle noise is the primary constraint in synthetic aperture radar (SAR) images. It will affect the features in the image and cause misinterpretation of objects. In this letter, curvelet with controlled shrinking technique is proposed to suppress speckle noise in SAR images. In this method, the curvelet transform is applied to SAR image. It decomposes image into different levels based on frequency. Application of using threshold and controlled shrinking function in the curvelet transform suppresses the noise coefficients. The results are favourable compared with some traditional techniques in terms of performance as well as preserving the details of the image.     

Wavelet-based speckle noise reduction in ultrasound B-scan images

Speckle noise is known to be signal-dependent in ultrasound imaging. Hence, separating noise from signal becomes a difficult task. This paper describes a wavelet-based method for reducing speckle noise. We derive from the model of the displayed ultrasound image the optimal wavelet-domain filter in the least mean-square sense. Simulations on synthetic data have been carried out in order to assess the performance of the proposed filter with regards to the classical wavelet shrinkage scheme, while phantom and tissue images have been used for testing it on real data. The results show that the filter effectively reduces the speckle noise while preserving resolvable details.     

Gaussianization of Diffusion MRI Data Using Spatially Adaptive Filtering

Diffusion MRI magnitude data, typically Rician or noncentral $\chi$ distributed, is affected by the noise floor, which falsely elevates signal, reduces image contrast, and biases estimation of diffusion parameters. Noise floor can be avoided by extracting real-valued Gaussian-distributed data from complex diffusion-weighted images via phase correction, which is performed by rotating each complex diffusion-weighted image based on its phase so that the actual image content resides in the real part. The imaginary part can then be discarded, leaving only the real part to form a Gaussian-noise image that is not confounded by the noise floor. The effectiveness of phase correction depends on the estimation of the background phase associated with factors such as brain motion, cardiac pulsation, perfusion, and respiration. Most existing smoothing techniques, applied to the real and imaginary images for phase estimation, assume spatially-stationary noise. This assumption does not necessarily hold in real data. In this paper, we introduce an adaptive filtering approach, called multi-kernel filter (MKF), for image smoothing catering to spatially-varying noise. Inspired by the mechanisms of human vision, MKF employs a bilateral filter with spatially-varying kernels. Extensive experiments demonstrate that MKF significantly improves spatial adaptivity and outperforms various state-of-the-art filters in signal Gaussianization.     

A novel local variance-based filtering method for denoising remote sensing images

In this paper, a novel filtering method is designed for denoising remote sensing image. Firstly, the image domain of noisy image is partitioned into blocks for estimating the variance of Gaussian white noise. Secondly, based on the fact that the variance of the textural region is always larger than that of the homogeneous region, the noisy image is roughly divided into homogeneous and textural regions. Thirdly, a novel filter is designed and is used to reduce the noises. To this end, adaptive windows with appropriate shape and size are selected for each pixel. With the pixels in the window(s), the noise intensity of the central pixel is estimated and further qualified as a noise level. Based on noise levels, pixel values within the filter window are first updated and then filtered by using the proposed filter. Compared with other filtering methods, better performance is achieved in both noise smoothing and detail conserving.     

Effects of adaptive speckle filtering on the diagnostic accuracy of ultrasound in vivo tissue characterization

Objective: The purpose of this study was to investigate whether there is an influence of adaptive speckle filtering on ultrasonic tissue characterization. Methods: We have implemented two adaptive two-dimensional speckle filters, the multivariate comparator and the local statistics filter. A tissue characterization system was used to classify ultrasound images showing diffuse liver disease. We compared the classification results achieved before and after speckle filtering. Results: The image texture was completely smoothed out and the classification results got worse when the local statistics filter had been used for adaptive speckle filtering. On the other hand, the image texture was preserved to a certain degree and the classification results were equally good or even slightly improved when the multivariate comparator had been used. In addition, the performance of both filters was demonstrated when they were applied to ultrasound images showing focal liver disease. Conclusion: The two adaptive speckle filters we have investigated differ in their effect on the image texture. The classification results of ultrasonic tissue characterization are reversely influenced by the two adaptive speckle filters. This can be explained with the presence of relevant information in the speckle pattern of homogeneous tissue in ultrasound images.     

自适应非局域均值滤波的超声图像去噪

目的 提出一种去除超声图像噪声的新方法。方法 对超声图像进行非局域搜索,找到相似的图像块进行加权平均,降低噪声。通过定义一个特征强度,区分斑点噪声和图像边界;然后将特征强度引入非局域滤波方法中,对平坦区域和边界进行自适应滤波。结果 本方法可有效去除斑点噪声,提高噪声图像的峰值信噪比(PSNR)和结构相似度指数(SSIM),优于常规方法。结论 自适应非局域均值滤波可有效去噪,并保护超声图像特征。     

超声乳腺肿瘤图像的边缘提取

目的探求乳腺肿瘤超声图像的边缘提取。方法广义梯度矢量流Snake模型已经成功地用于噪声相对比较小的CT、MRI等医学图像,然而乳腺肿瘤超声图像对比度低,斑点噪声大,很难将该模型直接应用于乳腺肿瘤超声图像。本文针对乳腺肿瘤超声图像的特点如图像对比度低,斑点噪声大,部分边缘缺失,肿瘤内部微细结构分布复杂(如血管,钙化灶等),特别恶性肿瘤还具有复杂形状等,采用相应的图像处理技术如非线性各向异性扩散滤除斑点噪声,形态学滤波器平滑图像,直方图均衡化提高图像的对比度,最后将该模型引入到乳腺肿瘤超声图像边缘提取。结果实验对158例乳腺肿瘤超声图像进行边缘提取,定量和定性分析均获得满意的结果。结论本文方法可以有效地用于超声乳腺肿瘤图像的边缘提取。     

Edge-preserving speckle texture removal by interference-based speckle filtering followed by anisotropic diffusion

Ultrasonography has an inherent noise pattern, called speckle, which is known to hamper object recognition for both humans and computers. Speckle noise is produced by the mutual interference of a set of scattered wavefronts. Depending on the phase of the wavefronts, the interference may be constructive or destructive, which results in brighter or darker pixels, respectively. We propose a filter that minimizes noise fluctuation while simultaneously preserving local gray level information. It is based on steps to attenuate the destructive and constructive interference present in ultrasound images. This filter, called interference-based speckle filter followed by anisotropic diffusion (ISFAD), was developed to remove speckle texture from B-mode ultrasound images, while preserving the edges and the gray level of the region. The ISFAD performance was compared with 10 other filters. The evaluation was based on their application to images simulated by Field II (developed by Jensen et al.) and the proposed filter presented the greatest structural similarity, 0.95. Functional improvement of the segmentation task was also measured, comparing rates of true positive, false positive and accuracy. Using three different segmentation techniques, ISFAD also presented the best accuracy rate (greater than 90% for structures with well-defined borders).     

Segmentation of the heart muscle in 3-D pediatric echocardiographic images

This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.     

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

Purpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.     

Kernel regression based feature extraction for 3D MR image denoising

Kernel regression is a non-parametric estimation technique which has been successfully applied to image denoising and enhancement in recent times. Magnetic resonance 3D image denoising has two features that distinguish it from other typical image denoising applications, namely the tridimensional structure of the images and the nature of the noise, which is Rician rather than Gaussian or impulsive. Here we propose a principled way to adapt the general kernel regression framework to this particular problem. Our noise removal system is rooted on a zeroth order 3D kernel regression, which computes a weighted average of the pixels over a regression window. We propose to obtain the weights from the similarities among small sized feature vectors associated to each pixel. In turn, these features come from a second order 3D kernel regression estimation of the original image values and gradient vectors. By considering directional information in the weight computation, this approach substantially enhances the performance of the filter. Moreover, Rician noise level is automatically estimated without any need of human intervention, i.e. our method is fully automated. Experimental results over synthetic and real images demonstrate that our proposal achieves good performance with respect to the other MRI denoising filters being compared.     

PerceptFlow: Real-Time Ultrafast Doppler Image Enhancement Using Deep Convolutional Neural Network and Perceptual Loss

Ultrafast ultrasound is an emerging imaging modality derived from standard medical ultrasound. It allows for a high spatial resolution of 100 μm and a temporal resolution in the millisecond range with techniques such as ultrafast Doppler imaging. Ultrafast Doppler imaging has become a priceless tool for neuroscience, especially for visualizing functional vascular structures and navigating the brain in real time. Yet, the quality of a Doppler image strongly depends on experimental conditions and is easily subject to artifacts and deterioration, especially with transcranial imaging, which often comes at the cost of higher noise and lower sensitivity to small blood vessels. A common solution to better visualize brain vasculature is either accumulating more information, integrating the image over several seconds or using standard filter-based enhancement techniques, which often over-smooth the image, thus failing both to preserve sharp details and to improve our perception of the vasculature. In this study we propose combining the standard Doppler accumulation process with a real-time enhancement strategy, based on deep-learning techniques, using perceptual loss (PerceptFlow). With our perceptual approach, we bypass the need for long integration times to enhance Doppler images. We applied and evaluated our proposed method on transcranial Doppler images of mouse brains, outperforming state-of-the-art filters. We found that, in comparison to standard filters such as the Gaussian filter (GF) and block-matching and 3-D filtering (BM3D), PerceptFlow was capable of reducing background noise with a significant increase in contrast and contrast-to-noise ratio, as well as better preserving details without compromising spatial resolution.     

Speckle noise reduction in ultrasound biomedical B-scan images using discrete topological derivative

Over three decades, several despeckling techniques have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information. The topological derivative (TD) is the recently adopted technique in the area of biomedical image processing. In this work, we computed the topological derivative for an appropriate function associated to the ultrasound B-scan image gradient by assigning a diffusion factor k, which indicates the cost endowed to that particular image. In this article, a novel image denoising approach, called discrete topological derivative (DTD) has been implemented. The algorithm has been developed in MATLAB7.1 and tested over 200 ultrasound B-scan images of several organs such as the liver, kidney, gall bladder and pancreas. Further, the performance of the DTD algorithm has been estimated by calculating important performance metrics. A comparative study was carried out between the DTD and the traditional despeckling techniques. The calculated peak signal-to-noise ratio (PSNR) (the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation) value of the DTD despeckled liver image is found to be 28 which is comparable with the outperformed speckle reducing anisotropic diffusion (SRAD) filter. SRAD filter is an edge-sensitive diffusion method for speckled images of ultrasonic and radar imaging applications. Canny edge detection and visual inspection of DTD filtered images by the trained radiologist found that the DTD algorithm preserves the hypoechoic and hyperechoic regions resulting in improved diagnosis as well as tissue characterization.     

Breast Ultrasound Despeckling Using Anisotropic Diffusion Guided by Texture Descriptors

Breast ultrasound (BUS) is considered the most important adjunct method to mammography for diagnosing cancer. However, this image modality suffers from an intrinsic artifact called speckle noise, which degrades spatial and contrast resolution and obscures the screened anatomy. Hence, it is necessary to reduce speckle artifacts before performing image analysis by means of computer-aided diagnosis systems, for example. In addition, the trade-off between smoothing level and preservation of lesion contour details should be addressed by speckle reduction schemes. In this scenario, we propose a BUS despeckling method based on anisotropic diffusion guided by Log–Gabor filters (ADLG). Because we assume that different breast tissues have distinct textures, in our approach we perform a multichannel decomposition of the BUS image using Log–Gabor filters. Next, the conduction coefficient of anisotropic diffusion filtering is computed using texture responses instead of intensity values as stated originally. The proposed algorithm is validated using both synthetic and real breast data sets, with 900 and 50 images, respectively. The performance measures are compared with four existing speckle reduction schemes based on anisotropic diffusion: conventional anisotropic diffusion filtering (CADF), speckle-reducing anisotropic diffusion (SRAD), texture-oriented anisotropic diffusion (TOAD), and interference-based speckle filtering followed by anisotropic diffusion (ISFAD). The validity metrics are the Pratt’s figure of merit, for synthetic images, and the mean radial distance (in pixels), for real sonographies. Figure of merit and mean radial distance indices should tend toward ‘1’ and ‘0’, respectively, to indicate adequate edge preservation. The results suggest that ADLG outperforms the four speckle removal filters compared with respect to simulated and real BUS images. For each method—ADLG, CADF, SRAD, TOAD and ISFAD—the figure of merit median values are 0.83, 0.40, 0.39, 0.51 and 0.59, and the mean radial distance median results are 4.19, 6.29, 6.39, 6.43 and 5.88.     

In Vivo Lateral Blood Flow Velocity Measurement Using Speckle Size Estimation

In previous studies, we proposed blood measurement using speckle size estimation, which estimates the lateral component of blood flow within a single image frame based on the observation that the speckle pattern corresponding to blood reflectors (typically red blood cells) stretches (i.e., is “smeared”) if blood flow is in the same direction as the electronically controlled transducer line selection in a 2-D image. In this observational study, the clinical viability of ultrasound blood flow velocity measurement using speckle size estimation was investigated and compared with that of conventional spectral Doppler of carotid artery blood flow data collected from human patients in vivo. Ten patients (six male, four female) were recruited. Right carotid artery blood flow data were collected in an interleaved fashion (alternating Doppler and B-mode A-lines) with an Antares Ultrasound Imaging System and transferred to a PC via the Axius Ultrasound Research Interface. The scanning velocity was 77 cm/s, and a 4-s interval of flow data were collected from each subject to cover three to five complete cardiac cycles. Conventional spectral Doppler data were collected simultaneously to compare with estimates made by speckle size estimation. The results indicate that the peak systolic velocities measured with the two methods are comparable (within ±10%) if the scan velocity is greater than or equal to the flow velocity. When scan velocity is slower than peak systolic velocity, the speckle stretch method asymptotes to the scan velocity. Thus, the speckle stretch method is able to accurately measure pure lateral flow, which conventional Doppler cannot do. In addition, an initial comparison of the speckle size estimation and color Doppler methods with respect to computational complexity and data acquisition time indicated potential time savings in blood flow velocity estimation using speckle size estimation. Further studies are needed for calculation of the speckle stretch method across a field of view and combination with an appropriate axial flow estimator.     

X-ray fluoroscopy noise modeling for filter design

Purpose

Fluoroscopy is an invaluable tool in various medical practices such as catheterization or image-guided surgery. Patient’s screen for prolonged time requires substantial reduction in X-ray exposure: The limited number of photons generates relevant quantum noise. Denoising is essential to enhance fluoroscopic image quality and can be considerably improved by considering the peculiar noise characteristics. This study presents analytical models of fluoroscopic noise to express the variance of noise as a function of gray level, a practical method to estimate the parameters of the models and a possible application to improve the performance of noise filtering.

Methods

Quantum noise is modeled as a Poisson distribution and results strongly signal-dependent. However, fluoroscopic devices generally apply gray-level transformations (i.e., logarithmic-mapping, gamma-correction) for image enhancement. The resulting statistical transformations of the noise were analytically derived. In addition, a characterization of the statistics of noise for fluoroscopic image differences was offered by resorting to Skellam distribution. Real fluoroscopic sequences of a simple step-phantom were acquired by a conventional fluoroscopic device and were utilized as actual noise measurements to compare with. An adaptive spatio-temporal filter based on the local conditional average of similar pixels has been proposed. The gray-level differences between the local pixel and the neighboring pixels have been assumed as measure of similarity. Filter performance was evaluated by using real fluoroscopic images of a step phantom and acquired during a pacemaker implantation.

Results

The comparison between experimental data and the analytical derivation of the relationship between noise variance and mean pixel intensity (noise-parameter models) were presented relatively to raw-images, after applying logarithmic-mapping or gamma-correction and for difference images. Results have confirmed a great agreement (adjusted R-squared values >  0.8). Clipping effects of real sensors were also addressed. A fine image restoration has been obtained by using a conditioned spatio-temporal average filter based on the noise statistics previously estimated.

Discussion

Fluoroscopic noise modeling is useful to design effective procedures for noise estimation and image filtering. In particular, filter performance analysis has showed that the knowledge of the noise model and the accurate estimate of noise characteristics can significantly improve the image restoration, especially for edge preserving. Fluoroscopic image enhancement can support further X-ray exposure reduction, medical image analysis and automated object identification (i.e., surgery tools, anatomical structures).