An improved SAR interferogram denoising method based on principal component analysis and the Goldstein filter

Interferogram filtering is an important data processing step in Interferometric synthetic aperture radar (InSAR) applications, which has a direct impact on the accuracy of the phase unwrapping and digital elevation model (DEM) or deformation results retrieval. An improved synthetic aperture radar (SAR) interferogram denoising method based on principal component analysis and the Goldstein filter is proposed, which can improve the coherence of interferogram remarkably and get more coherent targets. First, homogeneous pixels are identified with stacks of SAR amplitude data, which can obtain the unbiased coherence estimation. Then, the noise phase of one resolution unit is suppressed based on the principal component analysis of multi-baseline InSAR coherence stacks by considering the relationship between pixel size and scattering mechanism. Finally, the remaining noise is smoothed with the iterative Goldstein filter over spatial domain. The proposed method is tested over one deformed and low-coherence region to verify the better performance in the terms of noise reduction and coherence increase.     

超声图像中基于经验模态分解自动提取内-中膜厚度

目的为改善传统人工标记测量血管内-中膜厚度(IMT)的准确性和稳定性,提出基于图像分割技术的经验模态分解(EMD)改进算法。方法采用EMD改进算法去噪,根据血管壁的特点,在其中的极值点插值步骤使用非均匀的二维B样条函数,在水平和垂直方向上控制网格的密度不同,分别满足不同的分辨精度和平滑程度要求,改进了原始的二维EMD算法;然后通过K均值方法从图像中分离出血管腔、血管壁和其他组织,使用数学形态学算法逐步得到最终的内-中膜组织分割结果。结果改进EMD算法取得了较好的重建和滤波效果,有效克服了超声图像的强噪声和低分辨力对图像分割的限制,整个算法分割比较准确,算法复杂度相对较小。结论改进EMD算法是在超声图像中自动提取内-中膜的较有潜力的方法,能有效去除超声噪声,同时保留条纹结构的细节和边缘信息,有望于其他强噪声环境下提取条纹结构。     

Empirical mode decomposition and tissue harmonic imaging

Empirical mode decomposition (EMD) is a relatively new technique used in the analysis of nonlinear and nonstationary time series. Previous signal-processing methods used for medical ultrasound have been based on the assumption of a linear time-invariant system. More recently, the technique of tissue harmonic imaging (THI) has become prevalent. This technique relies on the nonlinear propagation of the sound wave through the medium to disperse the signal energy into the harmonic frequencies of the transmitted signal. In this paper, results are presented from using EMD to process received ultrasound echo signals that have passed through nonlinear media. The Hilbert spectrum is used to demonstrate an interpretation of the physical process underlying THI that is based on the concept of intrawave frequency modulation, rather then the spreading of signal energy into harmonic frequencies. The technique of EMD is shown to be able to produce superior results to the bandpass filtering method of THI, even when the band width of the transducer was such that the second harmonic would be suppressed.     

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.     

Filtering SAR interferometric phase noise using a split-window model

For interferometric synthetic aperture radar (InSAR) processing, the features of interferometric phase obtained in different coherence regions usually differ from each other. This is called region effect and exists in InSAR coherence map. When coherence value is used as a parameter to filter the phase noise, the result will be highly affected by this region effect. In this paper, we propose a new method of filtering InSAR phase noise using a split-window model. The idea of this method is to incorporate several filters into the model. Different filters will be used when dealing with phase noise locates in different coherence regions. The over-filtering or under-filtering caused by the coherence region effect can be eliminated in this method. As an example to demonstrate the superiority of this method, we incorporated an improved Goldstein filter and empirical mode decomposition filter into the current model. They were included to control phase noise level in the low- and high-coherence regions, respectively. The quantitative results obtained using a COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed) image pair over Kilauea volcano in Hawaiian demonstrate the advantages of the newly developed split-window model in filtering different types of noise.     

基于对象识别和分割的图像噪声抑制

在医学图像降噪中,不损失图像细节是至关重要的.传统的方法是直接对整个图像进行滤波处理.然而这种操作在减少噪声的同时也会破坏图像的细节.因此,关键的问题就是怎样在保持图像细节的同时能够减少噪声.为了解决上面提到的问题,在这篇论文中提出了一种模拟生物视觉的图像处理方法,首先提取图像的边缘信息,将图像分割成边缘区域和非边缘区域,然后对这两个区域采取不同的滤波降噪,再进行重新合成.与传统方法相比,本方法在保存图像细节上有一定的效果.     

F—X域复数经验模态分解去噪方法

常规的随机噪声衰减方法不能很好地适应非平稳地震信号的 处理,为此提出了一种基于F—X域投影法复数经验模态分解的预测滤波方法。采用基于空间投影的复数经验模态分解方法将F—X域地震数据分解为不同的模态分量;对不同模态的地震数据分别进行F—X域预测滤波。该方法克服了常规经验模态分解方法需要对复数信号的实部和虚部分别进行处理的缺陷,也避免了因剔除第一模态分量对有效信号的损失,具有较高的去噪能力和保幅性能。     

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.     

Image filtering via generalized scale

In medical imaging, low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR) often cause many image processing algorithms to perform poorly. Postacquisition image filtering is an important off-line image processing approach widely employed to enhance the SNR and CNR. A major drawback of many filtering techniques is image degradation by diffusing/blurring edges and/or fine structures. In this paper, we introduce a scale-based filtering method that employs scale-dependent diffusion conductance to perform filtering. This approach utilizes novel object scale information via a concept called generalized scale, which imposes no shape, size, or anisotropic constraints unlike previously published ball scale-based filtering strategies. The object scale allows us to better control the filtering process by constraining smoothing in regions with fine details and in the vicinity of boundaries while permitting effective smoothing in the interior of homogeneous regions. A new quantitative evaluation strategy that captures the SNR to CNR trade-off behavior of filtering methods is presented. The evaluations based on the Brainweb data sets show superior performance of generalized scale-based diffusive filtering over two existing methods, namely, ball scale-based and nonlinear complex diffusion processes. Qualitative experiments based on both phantom and patient magnetic resonance images demonstrate that the generalized scale-based approach leads to better preservation of fine details and edges.     

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).     

基于各向异性扩散和小波变换的磁共振图像降噪和增强方法

降噪是医学图像处理中一个非常重要的问题,传统去噪方法在降低噪声的同时会模糊图像的边缘,各向异性扩散滤波在降低图像噪声的同时能够使图像的边缘得到保持.利用小波变换可以对图像进行多尺度分解,使我们可以在不同尺度上对图像进行处理.本文利用各向异性扩散滤波对MRI图像进行降噪,然后利用平稳小波变换对图像进行增强处理.实验结果表明,该方法在有效去除噪声的同时能够增强图像的细节,有效地提高了图像的质量.     

Evaluation of mixed effects in event-related fMRI studies: impact of first-level design and filtering

With the introduction of event-related designs in fMRI, it has become crucial to optimize design efficiency and temporal filtering to detect activations at the 1st level with high sensitivity. We investigate the relevance of these issues for fMRI population studies, that is, 2nd-level analysis, for a set of event-related fMRI (er-fMRI) designs with different 1st-level efficiencies, adopting three distinct 1st-level filtering strategies as implemented in SPM99, SPM2, and FSL3.0. By theory, experiments, and simulations using physiological fMRI noise, we show that both design and filtering impact the outcome of the statistical analysis, not only at the 1st but also at the 2nd level. There are several reasons behind this finding. First, sensitivity is affected by both design and filtering, since the scan-to-scan variance, that is the fixed effect, is not negligible with respect to the between-subject variance, that is the random effect, in er-fMRI population studies. The impact of the fixed effects error on the sensitivity of the mixed effects analysis can be mitigated by an optimal choice of er-fMRI design and filtering. Moreover, the accuracy of the 1st- and 2nd-level parameter estimates also depend on design and filtering; especially, we show that inaccuracies caused by the presence of residual noise autocorrelations can be constrained by designs that have hemodynamic responses with a Gaussian distribution. In conclusion, designs with both good efficiency and decorrelating properties, for example, such as the geometric or Latin square probability distributions, combined with the "whitening" filters of SPM2 and FSL3.0, give the best result, both for 1st- and 2nd-level analysis of er-fMRI studies.     

Effects of digital filtering on the parameters of impulse-like eye movements

Influence of two filtering modes were researched with electro-oculographically recorded impulse-like eye movements. Linear finite impulse response (FIR) and nonlinear hybrid median filters were explored by considering gain and latency parameters that yield the most important information in the case of these eye movements. It is stated that carefully selected low pass filtering can securely be run without considerable change sin parameter values in order to discard noise stemming from physiological or other reasons. This revised version was published online in July 2006 with corrections to the Cover Date.     

k-means based hybrid wavelet and curvelet transform approach for denoising of remotely sensed images

This article presents a new technique for denoising of remotely sensed images based on multi-resolution analysis (MRA). Multi-resolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image processing and analysis. The multi-resolution image analysis methods have the ability to analyse the image in an adaptive manner, capturing local as well as global information. Further, noise, as one of the biggest obstacles for image analysis and for further processing, is effectively handled by multi-resolution methods. The article aims at the analysis of noise filtering of image using wavelets and curvelets methods on multispectral images acquired by the QuickBird and medium-resolution Landsat Thematic Mapper satellite systems. To improve the performance of noise filtering, an iterative thresholding scheme and a hybrid approach based on wavelet and curvelet transforms are proposed for restoring the image from its noisy version. Two comparative measures are used for evaluation of the performance of the methods for denoising. One of them is the peak signal-to-noise ratio and the second is the ability of the noise filtering scheme to preserve the sharpness of the edges. By both of these comparative measures, the hybrid approach of curvelet and wavelet for heterogeneous and homogeneous areas with iterative threshold has proved to be better than the others. Results are illustrated using QuickBird and Landsat images for proposed methods and compared with wavelets and curvelet-based denoising.     

ROC analysis of statistical methods used in functional MRI: individual subjects

The complicated structure of fMRI signals and associated noise sources make it difficult to assess the validity of various steps involved in the statistical analysis of brain activation. Most methods used for fMRI analysis assume that observations are independent and that the noise can be treated as white gaussian noise. These assumptions are usually not true but it is difficult to assess how severely these assumptions are violated and what are their practical consequences. In this study a direct comparison is made between the power of various analytical methods used to detect activations, without reference to estimates of statistical significance. The statistics used in fMRI are treated as metrics designed to detect activations and are not interpreted probabilistically. The receiver operator characteristic (ROC) method is used to compare the efficacy of various steps in calculating an activation map in the study of a single subject based on optimizing the ratio of the number of detected activations to the number of false-positive findings. The main findings are as follows: Preprocessing. The removal of intensity drifts and high-pass filtering applied on the voxel time-course level is beneficial to the efficacy of analysis. Temporal normalization of the global image intensity, smoothing in the temporal domain, and low-pass filtering do not improve power of analysis. Choices of statistics. the cross-correlation coefficient and t-statistic, as well as nonparametric Mann-Whitney statistics, prove to be the most effective and are similar in performance, by our criterion. Task design. the proper design of task protocols is shown to be crucial. In an alternating block design the optimal block length is be approximately 18 s. Spatial clustering. an initial spatial smoothing of images is more efficient than cluster filtering of the statistical parametric activation maps.     

A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme

A fully automatic, multiscale fuzzy C-means (MsFCM) classification method for MR images is presented in this paper. We use a diffusion filter to process MR images and to construct a multiscale image series. A multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels. The objective function of the conventional fuzzy C-means (FCM) method is modified to allow multiscale classification processing where the result from a coarse scale supervises the classification in the next fine scale. The method is robust for noise and low-contrast MR images because of its multiscale diffusion filtering scheme. The new method was compared with the conventional FCM method and a modified FCM (MFCM) method. Validation studies were performed on synthesized images with various contrasts and on the McGill brain MR image database. Our MsFCM method consistently performed better than the conventional FCM and MFCM methods. The MsFCM method achieved an overlap ratio of greater than 90% as validated by the ground truth. Experiments results on real MR images were given to demonstrate the effectiveness of the proposed method. Our multiscale fuzzy C-means classification method is accurate and robust for various MR images. It can provide a quantitative tool for neuroimaging and other applications.     

A UV-visible absorption spectrum denoising method based on EEMD and an improved universal threshold filter

When using ultraviolet-visible spectroscopy (UV-visible spectroscopy) to detect water quality parameters, the measured absorption spectrum signal often contains a lot of interference information. Therefore, denoising is extremely important in spectrum data processing and analysis, which directly affects the subsequent quantitative analysis and information mining. Choosing an appropriate denoising method is key to improve the spectral analysis accuracy and promote the spectral analysis ability. In this paper, a new UV-visible absorption spectrum denoising method is proposed: a denoising method based on ensemble empirical mode decomposition (EEMD) and improved universal threshold filtering (EEMD-based method). The noisy UV-visible absorption spectrum signal is firstly decomposed into a finite set of band limited signals called intrinsic mode functions (IMFs) via EEMD. Spearman''s rank correlation coefficient (Spearman''s rho) is then used as a criterion for the IMFs dominated by noise or useful signals, and the improved universal threshold filtering method is applied to the noise dominant IMFs to eliminate the noise. Finally, the denoised UV-visible absorption spectrum signal is reconstructed. In order to discuss the effectiveness of the EEMD-based denoising method proposed in this paper, we compare it with various wavelet-based threshold denoising methods. Both methods have been implemented on synthetic signals with diverse waveforms (‘Blocks’, ‘Bumps’ and ‘Heavy sine’). It is demonstrated that the proposed method outperforms the wavelet-based methods. Then, the measured UV-visible absorption spectra with different SNR were denoised by the wavelet and proposed methods. The method proposed also performs well in the spectrum denoising experiment.

When using ultraviolet-visible spectroscopy (UV-visible spectroscopy) to detect water quality parameters, the measured absorption spectrum signal often contains a lot of interference information.     

Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI

A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R(2)* and initial signal intensity (S(0)) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R(2)* and S(0) change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like "functional network" components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.     

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.     

Bias and variance in the estimate of the Doppler frequency induced by a wall motion filter

In pulsed Doppler flowmeters, processing of the Doppler signals is often done digitally. The first step in the analysis of the echoes is the filtering which is needed to remove stationary components and low frequency shifts induced by wall motion. This preliminary step is of utmost importance. The influence of uncorrelated noise on the measurement of Doppler signals at the input of this filter is analysed. The frequencies of the Doppler signals are extracted by an algorithm based on correlation techniques. We observed that the filter induces a correlated noise term, which results in an overestimation of the frequency. An effect similar to frequency aliasing may appear. The level of the bias is dependent on filter characteristics and noise level. Our study was carried out on simulated Doppler signals using first and second order filters. An especially desirable solution in flow mapping is proposed in order to decrease this error.