Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.     

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Carbon fiber reinforced plastics (CFRPs) have high specific stiffness and strength, but they are vulnerable to transverse loading, especially low-velocity impact loadings. The impact damage may cause serious strength reduction in CFRP structure, but the damage in a CFRP is mainly internal and microscopic, that it is barely visible. Therefore, this study proposes a method of determining impact damage in CFRP via poly(vinylidene fluoride) (PVDF) sensor, which is convenient and has high mechanical and electrical performance. In total, 114 drop impact tests were performed to investigate on impact responses and PVDF signals due to impacts. The test results were analyzed to determine the damage of specimens and signal features, which are relevant to failure mechanisms were extracted from PVDF signals by means of discrete wavelet transform (DWT). Support vector machine (SVM) was used for optimal classification of damage state, and the model using radial basis function (RBF) kernel showed the best performance. The model was validated through a 4-fold cross-validation, and the accuracy was reported to be 92.30%. In conclusion, impact damage in CFRP structures can be effectively determined using the spectral analysis and the machine learning-based classification on PVDF signals.     

基于DWT的彩色图像水印不可感知性评价

水印的不可感知性是评价水印算法性能优劣的重要指标之一,传统图像质量评价模型PSNR不能很好地评价水印化图像的质量.本文通过分析彩色图像中背景亮度、纹理、频率以及颜色对人类视觉的掩蔽,提出了一个基于DWT的彩色图像视觉掩蔽强度计算方法,并据此为彩色图像中各像素设置视觉质量掩蔽权值,得到了一个彩色图像水印不可感知性评价模型.实验结果表明新模型优于传统模型PSNR,其评价结果更接近人的视觉感觉.     

基于非冗余平移不变小波变换的磁共振图像降噪**☆

背景：MRI成像机制决定了其时间/空间分辨率和信噪比之间存在矛盾，因此图像降噪变得十分必要。目前基于离散小波变换的降噪方法广泛应用，然而存在平移敏感性的缺陷。目前已出现了克服平移敏感性的离散小波变换，但其冗余性导致计算复杂度的快速增加。 目的：针对图像降噪设计小波滤波器，减小降采样过程的影响，保持离散小波变换的非冗余性，并针对MRI图像Rician噪声的降噪进行分析。 方法：由于平移敏感性主要是由于离散小波变换分解时降采样产生的混叠项带来的，在保证非冗余的前提下，提出了通过减小混叠项的影响来减小平移敏感性。在此基础上，设计了一个双正交小波。最后，将其以常见的阈值降噪方法应用到磁共振图像Rician噪声的降噪中。 结果与结论：文章提出了设计小波滤波器的新方法，即满足严格重构条件外满足一些附加要求，最后将设计过程简化为一个有约束条件的最优化过程。将设计的双正交小波应用于MR图像，仿真结果表明降噪效果较通常小波有所改善，间接表明了设计思路和方法的有效性。     

Problematik einer frequenzspezifischen Schwellenmessung mit den Hirnstammpotentialen unter Verwendung des otometrischen Signals (DWT)

Zusammenfassung Bei dieser Versuchsreihe wurde untersucht, ob bei Beschallung mit dem otometrischen Signal (damped wavetrain; DWT) eine frequenzspezifische Schwellenmessung mit den Hirnstammpotentialen möglich ist. Zu diesem Zwecke wurden erstens die Latenzkennlinien der Hirnstammpotentiale von zehn normalhörenden Versuchspersonen bei 1, 4 und 8 kHz erstellt, zweitens bei Patienten mit unterschiedlichem Innenohrschaden im Tonaudiogramm auch eine Schwellenmessung mit dem Hirnstammpotential IV (Jewett V) bei diesen drei Frequenzen durchgeführt. Die Ergebnisse zeigen, daß die Potentiale eine frequenzspezifische Information bei intaktem akustischem System enthalten. Dies kann anhand der Latenzverlängerung der Hirnstammpotentiale bei Änderung der Beschallungsfrequenz von 8 auf 4 und auf 1 kHz geschlossen werden. Bei dem Versuch einer frequenzspezifischen Schwellenmessung mit dem Hirnstammpotential IV bei Patienten mit steileren Abfällen und Senken im Tonaudiogramm ergaben sich aber Schwierigkeiten.     

基于小波变换的R波检测算法

介绍了一种将小波变换应用于ECG信号检测R波的算法。该算法主要是在离散小波变换和多分辨率分析原理的基础上，利用db1小波特有的时频域特性，运用Mallat快速算法对心电信号进行了3层小波分解，分别在2、3层小波分解的高频系数中选择一定的阈值作为R波的判定条件，实现R波检测。通过对MIT／BIH（Massachusetts Institute of Technology／Boston’s Beth Israel Hospital）心电数据库的R波检测，结果表明，该检测算法即使在噪声干扰和病态的情况下，也很容易实现对R波的准确检测和精确定位，具有相当高的定位精度，R波正确检测率高达到99．8％。     

基于非冗余平移不变小波变换的医学图像配准

配准是图像处理中一个非常重要的过程,但其运算量通常非常大。离散小波变换(discrete wavelet transform,DWT)已成为减小运算量的有效工具,然而,它对平移的敏感意味着原图像特征的微小位移在其小波分解子图中可能产生不可预料的变化。本文根据分析认为平移敏感性是由DWT中降采样过程引入的混叠造成的,并以此提出了设计小波滤波器的新方法。该方法通过减小混叠影响,从而减小了DWT的平移敏感性。设计结果表明此方法使DWT的平移敏感性得到了有效抑制,同时保持了非冗余性。最后,两个医学图像的配准过程表明了该小波的有效性和设计方法的合理性。     

Secured Telemedicine Using Region-Based Watermarking with Tamper Localization

Medical images exchanged over public networks require a methodology to provide confidentiality for the image, authenticity of the image ownership and source of origin, and image integrity verification. To provide these three security requirements, we propose in this paper a region-based algorithm based on multiple watermarking in the frequency and spatial domains. Confidentiality and authenticity are provided by embedding robust watermarks in the region-of-non-interest (RONI) of the image using a blind scheme in the discrete wavelet transform and singular value decomposition domain (DWT-SVD). On the other hand, integrity is provided by embedding local fragile watermarks in the region-of-interest (ROI) of the image using a reversible scheme in the spatial domain. The integrity provided by the proposed algorithm is implemented on a block-level of the partitioned-image, thus enabling localized detection of tampered regions. The algorithm was evaluated with respect to imperceptibility, robustness, capacity, and tamper localization capability, using MRI, Ultrasound, and X-ray gray-scale medical images. Performance results demonstrate the effectiveness of the proposed algorithm in providing the required security services for telemedicine applications.     

Time division multiplexing based method for compressing ECG signals: application for normal and abnormal cases

The proposed ECG compression method combines three major approaches based on time division multiplexing (TDM) and multilevel wavelet decomposition followed by parametrical modelling. Before applying these techniques, a pre-processing step is required, which consists of detecting and aligning different beats. Even though this compression method is regarded as a lossy method, we will show how a high compression ratio (CR) can be achieved by preserving the major medical information within the ECG. Several normal and abnormal signals from various databases are used to evaluate the performance of the proposed technique.     

Multiplexed Wavelet Transform Technique for Detection of Microcalcification in Digitized Mammograms

Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent–child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.Permission has been granted by the Departmental Research Committee, Department of Electronics, Cochin University of Science and Technology, for pursuing research in the field of automated detection of microcalcification in digitized mammograms.