基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。     

基于维纳滤波与小波相融合的超声医学图像去噪方法

目的 探求一种有效的超声医学图像去噪方法.方法 在分析维纳滤波和基于自适应前处理的多尺度小波非线性阈值斑点噪声抑制方法(MSSNT-A)的基础上,提出一种基于维纳滤波与MSSNT-A相融合的超声医学图像去噪方法.利用该方法首先对加噪图像分别进行维纳滤波和MSSNT-A去噪.然后提取维纳滤波处理后的图像边缘,再将其与MSSNT-A去噪后的图像的_柑应像素点进行融合,得到去噪图像.结果 有效地去除了斑点噪声,图像的细节得到保留.结论 与维纳滤波和MSSNT-A方法相比,该方法在有效去除斑点噪声的同时,很好地保留了图像边缘和图像细节信息.     

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.     

MR图像去噪算法研究进展

MRI对医学诊断有重要价值,而去噪是MR图像分析与诊断的基础问题之一.因此,探讨能消除MR图像噪声的方法具有重要的临床意义和应用价值.作者从空间域、变换域及多尺度分析等方面对MR图像去噪算法进行综述,分析和比较其性能,并对医学图像去噪领域的前景和趋势进行展望.     

诱发电位提取的子空间和小波去噪复合方法

目的:为减少提取诱发电位所需的试验次数,有效去除自发脑电噪声,提出一种新的视觉诱发电位提取方法并进行验证。方法:基于奇异值分解的子空间方法可以用于去除信号中的噪声。①其基本原理是,由含噪信号形成的数据矩阵进行奇异值分解可以获得信号子空间和噪声子空间,将含噪信号正交投影到信号子空间中即可得到去除噪声。因为在头皮测量得到的诱发电位记录信号的信噪比很低,所以仅使用基于奇异值分解的子空间方法来去除噪声并不能有效地提取诱发电位。②实验记录中对诱发电位成分影响较大的自发脑电是有色噪声,描述其奇异性的Lipschitz指数具有不确定性,可能为正,也可能为负,因此仅用小波去噪方法提取诱发电位也不能取得理想的结果。③为此,提出了一种基于奇异值分解的子空间正交投影和小波去噪复合方法来提取诱发电位。首先应用基于奇异值分解的子空间方法将包含噪声的记录信号分解为信号子空间和噪声子空间,将含噪信号投影到信号子空间可得到初步去噪的信号,再应用小波变换进一步去除噪声,即可提取诱发电位。结果:采用自发脑电模型产生有色的自发脑电噪声,与白噪声一起加入仿真的诱发脑电信号中,在低信噪比小于-10dB的情况下,可有效地提取出诱发脑电信号。仿真和实验结果表明这种复合方法的效果好于单独采用其中的一种方法,能将提取诱发电位的实验次数由20次左右缩短为四五次。结论:将基于奇异值分解的子空间方法和小波去噪结合起来,能有效提取诱发电位,减少提取诱发电位所需的试验次数。     

基于小波变换的数字胸片增强

目的 研究一种基于小波变换的数字胸片图像增强新算法.方法 小波分解后,首先利用小波阈值法进行去噪预处理,然后对高频分量采用非线性增强,对低频分量采用反锐化掩模增强方法,通过小波反变换重构出增强后的图像.结果 通过对传统增强方法和本文提出的小波增强新方法进行实验对比,验证了本文算法对数字胸片图像有较好的增强效果.结论 对于分辨力低、噪声干扰严重、光照不均的数字胸片图像,本文提出的基于小波变换的增强新方法可保留图像细节信息,同时有效去除噪声.     

基于提升小波变换的功能MRI数据分析

目的 构建一种快速的fMRI数据小波分析方法.方法 用提升小波变换代替平稳小波变换分解fMRI数据,以分离其实验响应信号和干扰信号,再由频谱分析识别实验响应信号所在的小波尺度,并只对实验响应信号所在的小波尺度进行重构,最后对重构信号进行相关分析来检测激活.结果 分析视觉实验数据显示,在显著性水平为α＜10-6时,本文基于提升小波变换的方法比未去噪的相关分析方法更灵敏,而消耗时间比基于平稳小波变换的方法大幅度减少.同时本文方法只需24个数据点即可进行小波重构,而基于平稳小波变换的方法则需要256个数据点.结论 本文为fMRI数据提出了一种既能快速分析又能有效压缩的小波分析方法.     

医学图像处理中的小波变换应用

医学图像在现代医疗诊断中有重要作用,近年来小波变换在医学图像处理如边缘提取、去噪、增强、图像压缩、融合等方面得到了广泛的应用.本文利用小波变换的理论分析和相关文献报道对以上处理方法进行综述.     

轮廓波及曲波和小波变换用于显微图像消噪的比较

背景:小波变换只能反映信号的零维奇异性,无法最优表示图像中的线奇异;而且小波变换只存在3个方向,这些都显著影响了它在图像处理领域的应用效果.针对小波变换的缺点,多尺度几何分析理论正在逐步发展,轮廓波变换和曲波变换就是其中的典型代表.目的:定性、定量地比较轮廓波、曲波和小波变换在图像消噪处理中的效果.方法:在简要介绍3种变换基本原理的基础上,比较它们在图像消噪领域的应用,以均方误差和峰值信噪比作为定量指标评价消噪效果,并将其应用于显微镜图像的消噪处理.结果与结论:综合定量评价指标和人眼视觉感受,曲波变换的消噪结果最佳,轮廓波变换效果次之,小波变换效果则不够理想.     

结合小波变换对心电信号中RR间期检测的一种新方法

目的:探讨改善心电信号噪声干扰的方法,提高对心率变异信号中的RR间期检测精度。方法:①采用国际上通用的MIT/BIH数据库作为研究心率变异信号中RR间期的研究对象,选取其中的代表性数据组进行实验分析(受严重基线漂移影响的正常心电信号101.dat,大量室性期前收缩的心电信号105.dat,无噪声干扰的正常心电信号213.dat,心动过速的心电信号217.dat)。②利用小波变化方法结合Mallat算法对含有噪声的心电信号进行多尺度分析和信号重构,并利用R波的自身波形特点,采用相对周期极大值法来进行R波位置检测,进而计算RR间期序列值。结果:利用小波变换对含有噪声的信号进行噪声消除可以达到在很大程度保留原始信号的波形特征的同时又取得良好消噪效果的目的。能够显著减小工频干扰、基线漂移和肌电干扰等噪声对判别的影响。通过MIT/BIH数据库中四组有代表性特征的心电信号进行研究,发现采用相对周期极大值检测法可以显著减少检测中易出现的漏检和误检现象,快速而准确的获得RR间期的序列值。结论:小波变换法能够显著减少噪声对信号的干扰,特别是离散小波的应用使数字信号的处理由理论走向实际,结合Mallat快速算法,使得小波变换完全走向实用化。周期极大值法对心率变异信号中RR间期的检测有较好的精确度和快速性。     

Dual tree complex wavelet transform based denoising of optical microscopy images

Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.OCIS codes: (100.0100) Image processing, (100.7410) Wavelets, (100.3020) Image reconstruction-restoration     

Wavelet denoising of displacement estimates in elastography

Wavelet shrinkage denoising of the displacement estimates to reduce noise artefacts, especially at high overlaps in elastography, is presented in this paper. Correlated errors in the displacement estimates increase dramatically with an increase in the overlap between the data segments. These increased correlated errors (due to the increased correlation or similarity between consecutive displacement estimates) generate the so-called "worm" artefact in elastography. However, increases in overlap on the order of 90% or higher are essential to improve axial resolution in elastography. The use of wavelet denoising significantly reduces errors in the displacement estimates, thereby reducing the worm artefacts, without compromising on edge (high-frequency or detail) information in the elastogram. Wavelet denoising is a term used to characterize noise rejection by thresholding the wavelet coefficients. Worm artefacts can also be reduced using a low-pass filter; however, low-pass filtering of the displacement estimates does not preserve local information such as abrupt change in slopes, causing the smoothing of edges in the elastograms. Simulation results using the analytic 2-D model of a single inclusion phantom illustrate that wavelet denoising produces elastograms with the closest correspondence to the ideal mechanical strain image. Wavelet denoising applied to experimental data obtained from an in vitro thermal lesion phantom generated using radiofrequency (RF) ablation also illustrates the improvement in the elastogram noise characteristics.     

fMRI数据去噪处理对FastICA检测精度的影响

目的 探讨fMRI数据中白噪声及去噪预处理方法对于采用FastICA算法检测人脑激活区精度的影响。方法 采用模拟和真实fMRI数据进行测试。实验重复50次,以比较未去噪、高斯平滑和两种正交小波方法去噪预处理后FastICA算法分离激活信号的能力。对不同阈值条件下检测的激活体素进行统计分析。用ROC曲线评价检测质量。结果 模拟实验显示,当fMRI数据的SNR为15 db时,去噪预处理能显著提高FastICA检测准确率;SNR为20 db时,去噪对提高FastICA检测准确率影响较小。采用不同的小波基及不同参数去噪处理结果差异较大,存在经db4小波去噪处理后激活区检测结果的敏感度和特异度反而降低的现象,但随着激活区减小,这种现象消失。结论 对于FastICA而言,小波去噪与传统的高斯平滑方法相比未显示出更好的敏感度和特异度;在低SNR情况下,有必要进行去噪预处理;在高SNR情况下,不恰当的去噪方法反而可导致FastICA检测精度降低。     

Wavelet-based edge detection in ultrasound images

We introduce a new wavelet-based method for edge detection in ultrasound (US) images. Each beam that is analyzed is first transformed into the wavelet domain using the continuous wavelet transform (CWT). Because the CWT preserves both scale and time information, it is possible to separate the signal into a number of scales. The edge is localized by first determining the scale at which the power spectrum, based on the wavelet transform, has its maximum value. Next, at this scale we find the position of the peak for the squared CWT. This method does not depend on any threshold, after the range of scales have been determined. We suggest a range of scales for US images in general. Sample edge detections are demonstrated in US images of straight and jagged edges of simple structures submerged in water bath, and of an abdominal aorta aneurysm phantom.     

Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D denoising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional denoising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the denoised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of denoised wavelet coefficients for each voxel. Given the de-correlated nature of these denoised wavelet coefficients, it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules: First, in the analysis module we combine a new 3-D wavelet denoising approach with signal separation properties of ICA in the wavelet domain. This step helps obtain an activation component that corresponds closely to the true underlying signal, which is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing+spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false positive voxels.     

Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation

Denoising of Positron Emission Tomography (PET) images is a challenging task due to the inherent low signal-to-noise ratio (SNR) of the acquired data. A pre-processing denoising step may facilitate and improve the results of further steps such as segmentation, quantification or textural features characterization. Different recent denoising techniques have been introduced and most state-of-the-art methods are based on filtering in the wavelet domain. However, the wavelet transform suffers from some limitations due to its non-optimal processing of edge discontinuities. More recently, a new multi scale geometric approach has been proposed, namely the curvelet transform. It extends the wavelet transform to account for directional properties in the image. In order to address the issue of resolution loss associated with standard denoising, we considered a strategy combining the complementary wavelet and curvelet transforms. We compared different figures of merit (e.g. SNR increase, noise decrease in homogeneous regions, resolution loss, and intensity bias) on simulated and clinical datasets with the proposed combined approach and the wavelet-only and curvelet-only filtering techniques. The three methods led to an increase of the SNR. Regarding the quantitative accuracy however, the wavelet and curvelet only denoising approaches led to larger biases in the intensity and the contrast than the proposed combined algorithm. This approach could become an alternative solution to filters currently used after image reconstruction in clinical systems such as the Gaussian filter.     

Weakly-supervised progressive denoising with unpaired CT images

Although low-dose CT imaging has attracted a great interest due to its reduced radiation risk to the patients, it suffers from severe and complex noise. Recent fully-supervised methods have shown impressive performances on CT denoising task. However, they require a huge amount of paired normal-dose and low-dose CT images, which is generally unavailable in real clinical practice. To address this problem, we propose a weakly-supervised denoising framework that generates paired original and noisier CT images from unpaired CT images using a physics-based noise model. Our denoising framework also includes a progressive denoising module that bypasses the challenges of mapping from low-dose to normal-dose CT images directly via progressively compensating the small noise gap. To quantitatively evaluate diagnostic image quality, we present the noise power spectrum and signal detection accuracy, which are well correlated with the visual inspection. The experimental results demonstrate that our method achieves remarkable performances, even superior to fully-supervised CT denoising with respect to the signal detectability. Moreover, our framework increases the flexibility in data collection, allowing us to utilize any unpaired data at any dose levels.