基于小波变换的混合二维ECG数据压缩方法

提出了一种新的基于小波变换的混合二维心电(electrocardiogram,ECG)数据压缩方法。基于ECG数据的两种相关性,该方法首先将一维ECG信号转化为二维信号序列。然后对二维序列进行了小波变换,并利用改进的编码方法对变换后的系数进行了压缩编码:即先根据不同系数子带的各自特点和系数子带之间的相似性,改进了等级树集合分裂(setpartitioninghierarchicaltrees,SPIHT)算法和矢量量化(vectorquantization,VQ)算法;再利用改进后的SPIHT与VQ相混合的算法对小波变换后的系数进行了编码。利用所提算法与已有具有代表性的基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明:所提算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比。     

小波变换及滚球算法在单分子荧光能量共振转移图像分析中的应用

单分子荧光共振能量转移技术是通过检测单个分子内的荧光供体及受体间荧光能量转移的效率来研究分子构象的变化．要得到这些生物大分子的信息就需要对大量的单分子信号进行统计分析,人工分析这些信息,既费时费力又不具备客观性和可重复性,因此本文将小波变换及滚球算法应用到单分子荧光能量共振转移图像中对单分子信号进行统计分析．在保证准确检测到单分子信号的前提下,文章对滚球算法和小波变换算法处理图像后的线性进行了分析,结果表明,滚球算法和小波变换算法不但能够很好地去除单分子FRET图像的背景噪声,同时还能很好地保持单分子荧光信号的线性．最后本文还利用滚球算法处理单分子FRET图像及统计15 bp DNA的FRET效率的直方图,通过计算得到了15 bp DNA的FRET效率值．     

小波变换及滚球算法在单分子荧光能量共振转移图像分析中的应用（英文）

单分子荧光共振能量转移技术是通过检测单个分子内的荧光供体及受体间荧光能量转移的效率来研究分子构象的变化.要得到这些生物大分子的信息就需要对大量的单分子信号进行统计分析,人工分析这些信息,既费时费力又不具备客观性和可重复性,因此本文将小波变换及滚球算法应用到单分子荧光能量共振转移图像中对单分子信号进行统计分析.在保证准确检测到单分子信号的前提下,文章对滚球算法和小波变换算法处理图像后的线性进行了分析,结果表明,滚球算法和小波变换算法不但能够很好地去除单分子FRET图像的背景噪声,同时还能很好地保持单分子荧光信号的线性.最后本文还利用滚球算法处理单分子FRET图像及统计15 bp DNA的FRET效率的直方图,通过计算得到了15 bp DNA的FRET效率值.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

基于平稳小波变换的EEG视觉的信号修正

眼球运动和眨眼会在眼球周围产生电信号,这种电信号的存在直接影响到对EEG信号的分析特征提取及EEG模式的分类等研究。本文提出了一种基于小波阈值滤噪方法来修正EEG信号中出现的视觉伪信号(OA)。这种用于EEG视觉伪信号处理的小波方法的实现过程如下：1)用平稳小波变换(SWT)对原始EEG信号进行处理;2)设置低频带信号的系数阈值;3)对滤噪后的信号进行重构。实验结果表明这种方法同时适用于眨眼和眼球运动产生的伪信号。最后,通过对采集的信号处理前后做了对比,说明其有效性。     

基于多尺度植物种子图像边缘检测算法的设计与实现

小波变换,由于其具有时频局部化的特性及多尺度特性,能敏感地反映突变信号,是一种理想的边缘提取方法．本文系统地介绍了作者在图像边缘检测方面所做的理论探讨、算法及应用研究工作．目前的边缘提取方法有多种,本文将重点集中于基于小波变换的图像边缘检测方法的理论推导和算法实现．     

改良的小波变换算法在生物微粒检测中的应用

荧光图像的微粒检测已经成为了生物学研究中不可或缺的工具之一．介绍了一种改良的小波变换算法(improved wavelet transform,IWT),该方法实现简单,能够以很高的速度和精度来进行生物微粒的检测．IWT源自多尺度小波乘积算法(wavelet multiscale products,WMP),但它不仅解决了WMP算法遇到的问题,而且在处理各类图像的时候具有更强的适应性．使用人工合成的图像和真实的图像来定量地分析IWT、WMP以及多尺度方差稳定变换算法(multiscale variance stabilizing transform,MSVST)的检测效果．实验结果表明, IWT在大多数情况下的检测效果比WMP好很多,且与更为复杂的MSVST算法相当．此外,在处理相同图像时,IWT的速度比MSVST快20%．因此,IWT算法能够普遍适用于各种生物微粒的自动化检测,其简单准确的特点使之成为荧光图像分析更好的选择．     

一种滤除医学影像噪声的混合滤波算法

目的:医学影像在获取、存储、传输过程中会不同程度地受到噪声污染,这极大影像了其在临床诊疗中的应用。为了有效地滤除医学影像噪声,提出了一种混合滤波算法。方法:该算法首先将含有高斯和椒盐噪声的图像进行形态学开运算,然后对开运算后的图像进行二维小波分解,得到高频和低频小波分解系数。保留低频系数不变,将高频系数经过维纳滤波器进行滤波,最后进行小波系数重构。结果:采用该混合滤波算法、小波阈值去噪、中值滤波、维纳滤波分别对含有混合噪声的医学影像分别进行滤除噪声处理,该滤波算法去噪后影像的PSNR值明显高于其他三种方法。结论:该混合滤波算法是一种较为有效的医学影像噪声滤除方法。     

一种滤除医学影像噪声的混合滤波算法

目的：医学影像在获取、存储、传输过程中会不同程度地受到噪声污染,这极大影像了其在临床诊疗中的应用。为了有效地滤除医学影像噪声,提出了一种混合滤波算法。方法：该算法首先将含有高斯和椒盐噪声的图像进行形态学开运算,然后对开运算后的图像进行二维小波分解,得到高频和低频小波分解系数。保留低频系数不变,将高频系数经过维纳滤波器进行滤波,最后进行小波系数重构。结果：采用该混合滤波算法、小波阚值去噪、中值滤波、维纳滤波分别对含有混合噪声的医学影像分别进行滤除噪声处理,该滤波算法去噪后影像的PSNR值明显高于其他三种方法。结论：该混合滤波算法是一种较为有效的医学影像噪声滤除方法。     

线性时频分析在ECG信号分析中的应用

目的:心电信号(Electrocardio-signal,ECG)是人体中最重要的生物信号之一,是一种具有非平稳性和非线性特性的信号.分析ECG信号是诊断心脏疾病的有利工具,近年来国内外很多学者致力于这方面的研究.本文探讨短时Fourier变换(STFT)和离散小波变换(DWT)这两种时频分析方法在ECG信号分析中的应用.方法:本文采用麻省理工学院的MIT-BIH数据库中提供的数据,运用MATLAB软件编程,讨论短时Fourier变换和离散小波变换在ECG信号分析中的应用.结果:通过编程,做出了正常ECG信号和失常ECG信号的短时Fourier变换的时域图和频谱图以及正常ECG信号和失常ECG信号的单级离散小波变换的结果.结论:正常ECG信号和失常ECG信号的STFT变换的时域图和频谱图都能反应出信号的频率和时间的变化关系.但是,正常信号和失常信号的频率和时间有明显不同,正常信号的能量随时间和频率的变化关系有序整齐,而且周围有较少的杂波;失常信号的能量随时间和频率的变化关系杂乱,而且周围存在较多的杂波.通过离散小波变化后,正常信号和失常信号均产生了不同的离散小波系数,根据不同的离散小波系数,可以很容易判断正常信号和失常信号的区别.     

Noise suppressed,multifocus image fusion for enhanced intraoperative navigation

Current intraoperative imaging systems are typically not able to provide ‘sharp’ images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal‐to‐noise‐ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub‐optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi‐level stationary wavelet transform with individual threshold‐based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Assessing potential errors of MRI-based measurements of pulmonary blood flow using a detailed network flow model

MRI images of pulmonary blood flow using arterial spin labeling (ASL) measure the delivery of magnetically tagged blood to an image plane during one systolic ejection period. However, the method potentially suffers from two problems, each of which may depend on the imaging plane location: 1) the inversion plane is thicker than the imaging plane, resulting in a gap that blood must cross to be detected in the image; and 2) ASL includes signal contributions from tagged blood in conduit vessels (arterial and venous). By using an in silico model of the pulmonary circulation we found the gap reduced the ASL signal to 64-74% of that in the absence of a gap in the sagittal plane and 53-84% in the coronal. The contribution of the conduit vessels varied markedly as a function of image plane ranging from ～90% of the overall signal in image planes that encompass the central hilar vessels to <20% in peripheral image planes. A threshold cutoff removing voxels with intensities >35% of maximum reduced the conduit vessel contribution to the total ASL signal to ～20% on average; however, planes with large contributions from conduit vessels underestimate acinar flow due to a high proportion of in-plane flow, making ASL measurements of perfusion impractical. In other image planes, perfusion dominated the resulting ASL images with good agreement between ASL and acinar flow. Similarly, heterogeneity of the ASL signal as measured by relative dispersion is a reliable measure of heterogeneity of the acinar flow distribution in the same image planes.     

Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach.     

基于双正交小波变换的宫缩信号消噪

小波分析的信号消噪方法是现代信号处理中的重要组成部分,小波基的不同选取将直接影响消噪的效果.本文在全局阈值的标准下,基于不同噪声水平,讨论了小波基的正交性和线性相位性对消噪结果的影响,提出了选取小波基的一般方法,最后利用双正交小波基在软阈值标准下实现了对宫缩信号的消噪处理,并取得了较好的效果.     

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is reconstructed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10 dB at SNR 5 dB, and most of the improvement SNR are better than other methods at least 1 dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9 km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively.     

High-definition mapping of neural activity using voltage-sensitive dyes

The distribution of patterns of activity in different brain structures has been related to the encoding and processing of sensory information. Consequently, it is important to be able to image the distribution of these patterns to understand basic brain functions. The spatial resolution of voltage-sensitive dye (VSD) methods has recently been enhanced considerably by the use of video imaging techniques. The main factor that now hampers the resolution of VSD patterns is the inherent limitation of the optical systems. Unfortunately, the intrinsic characteristics of VSD images impose important limitations that restrict the use of general deconvolution techniques. To overcomes this problem, in this study an image restoration procedure has been implemented that takes into consideration the limiting characteristics of VSD signals. This technique is based on applying a set of imaging processing steps. First, the signal-to-noise (S/N) ratio of the images was improved to avoid an increase in the noise levels during the deconvolution procedures. For this purpose, a new filter technique was implemented that yielded better results than other methods currently used in optical imaging. Second, focal plane images were deconvolved using a modification of the well-known nearest-neighbor deconvolution algorithm. But to reduce the light exposure of the preparation and simplify image acquisition procedures, adjacent image planes were modeled according to the in-focus image planes and the empirical point spread function (PSF) profiles. Third, resulting focal plane responses were processed to reduce the contribution of optical responses that originate in distant image planes. This method was found to be satisfactory under simulated and real experimental conditions. By comparing the restored and unprocessed images, it was clearly demonstrated that this method can effectively remove the out-of-focus artifacts and produce focal plane images of better quality. Evaluations of the tissue optical properties allowed assessment of the maximum practical optical section thickness using this deconvolution technique in the optical system tested. Determination of the three-dimensional PSF permitted the correct application of deconvolution algorithms and the removal of the contaminating light arising from adjacent as well as distant optical planes. The implementation of this deconvolution approach in salamander olfactory bulb allowed the detailed study of the laminar distribution of voltage-sensitive changes across the bulb layer. It is concluded that (1) this deconvolution procedure is well suited to deconvolved low-contrast images and offers important advantages over other alternatives; (2) this method can be properly used only when the tissue optical properties are first determined; (3) high levels of light scattering in the tissue reduce the optical section capabilities of this technique as well as other deconvolution procedures; and (4) use of the highest numerical aperture in the objectives is advisable because this improves not only the light-collecting efficiency to detect poor-contrast images, but also the spatial frequency differences between adjacent image planes. Under this condition it is possible to overcome some of the limitations imposed by the light scattering/birefringence of the tissue.     

Automated detection of elementary calcium release events using the á trous wavelet transform

We developed an algorithm for the automated detection and analysis of elementary Ca2+ release events (ECRE) based on the two-dimensional nondecimated wavelet transform. The transform is computed with the "à trous" algorithm using the cubic B-spline as the basis function and yields a multiresolution analysis of the image. This transform allows for highly efficient noise reduction while preserving signal amplitudes. ECRE detection is performed at the wavelet levels, thus using the whole spectral information contained in the image. The algorithm was tested on synthetic data at different noise levels as well as on experimental data of ECRE. The noise dependence of the statistical properties of the algorithm (detection sensitivity and reliability) was determined from synthetic data and detection parameters were selected to optimize the detection of experimental ECRE. The wavelet-based method shows considerably higher detection sensitivity and less false-positive counts than previously employed methods. It allows a more efficient detection of elementary Ca2+ release events than conventional methods, in particular in the presence of elevated background noise levels. The subsequent analysis of the morphological parameters of ECRE is reliably reproduced by the analysis procedure that is applied to the median filtered raw data. Testing the algorithm more rigorously showed that event parameter histograms (amplitude, rise time, full duration at half-maximum, and full width at half-maximum) were faithfully extracted from synthetic, "in-focus" and "out-of-focus" line scan sparks. Most importantly, ECRE obtained with laser scanning confocal microscopy of chemically skinned mammalian skeletal muscle fibers could be analyzed automatically to reproducibly establish event parameter histograms. In summary, our method provides a new valuable tool for highly reliable automated detection of ECRE in muscle but can also be adapted to other preparations.     

Computer detection of the rapid diffusion of fluorescent membrane fusion markers in images observed with video microscopy.

We have developed an algorithm for automated detection of the dynamic pattern characterizing flashes of fluorescence in video images of membrane fusion. The algorithm detects the spatially localized, transient increases and decreases in brightness that result from the dequenching of fluorescent dye in phospholipid vesicles or lipid-enveloped virions fusing with a planar membrane. The flash is identified in video images by its nonzero time derivative and the symmetry of its spatial profile. Differentiation is implemented by forward and backward subtractions of video frames. The algorithm groups spatially connected pixels brighter than a user-specified threshold into distinct objects in forward- and backward-differentiated images. Objects are classified as either flashes or noise particles by comparing the symmetries of matched forward and backward difference profiles and then by tracking each profile in successive difference images. The number of flashes identified depends on the brightness threshold, the size of the convolution kernel used to filter the image, and the time difference between the subtracted video frames. When these parameters are changed so that the algorithm identifies an increasing percentage of the flashes recognized by eye, an increasing number of noise objects are mistakenly identified as flashes. These mistaken flashes can be eliminated by a human observer. The algorithm considerably shortens the time needed to analyze video data. Tested extensively with phospholipid vesicle and virion fusion with planar membranes, our implementation of the algorithm accurately determined the rate of fusion of influenza virions labeled with the lipophilic dye octadecylrhodamine (R18).     

Integrating image quality in 2nu-SVM biometric match score fusion

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.