基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法

目的眨眼伪迹是脑电中一种常见且影响严重的伪迹。本论文提出一种基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法,无需眼电参考信号,做到自动去除单导脑电信号中的眨眼伪迹。方法首先利用小波奇异点检测特性以检测眨眼伪迹的峰值位置,然后只对眨眼伪迹区域进行小波阈值去噪。结果实验结果表明,本方法能够有效检测眨眼伪迹,避免了普通方法去噪时对非眨眼区域的影响。结论本方法使用的阈值和阈值函数优于典型的阈值和软、硬阈值函数,有效地去除了脑电中的眨眼伪迹。     

Processing technique for ultrasound images

Ultrasound images are sometimes difficult to reproduce repeatedly and perfectly when operating a complicated instrument, especially in regard to the amplifier gain. Thus the logarithm-differential processing (l.d.p.) method described here is aimed at an improvement in the uniformity of the clinical information. This paper discusses the use of the time gain control method and the role of a physical model of tissue, for example a parallel interface model, which has been used for the derivation of acoustical parameters from an echo signal. The l.d.p. method has the following advantages: (i) each point in the image corresponds with the local acoustical parameters of the tissue; (ii) the image will be insensitive to the variations in the gain of the amplifier; and (iii) the l.d.p. method is very easy to operate as a real-time system. A set of six clinical photographs is presented showing that a 20 dB variation in the gain of the amplifier may seriously affect the images. However, the processed images are insensitive to the value of amplifier gain.     

Interslice coding for medical three-dimensional images using an adaptive mode selection technique in wavelet transform domain

In this article the authors propose a novel interslice coding algorithm especially appropriate for medical 3-dimensional (3D) images. The proposed algorithm is based on a video coding algorithm using motion estimation/compensation and transform coding. In the algorithm, warping is adopted for motion compensation. Then, by using adaptive mode selection, an MC residual image and original image are mixed up in the wavelet transform domain for improvement in coding performance. The mixed image is then compressed by the zerotree coding method. It is proven that the adaptive mode selection technique in the wavelet transform domain is very useful for medical 3D image coding. Simulation results show that the proposed scheme provides good performance, regardless of interslice distance, and is prospective for medical 3D image compression.     

小波变换的阈值选取及其在细胞图像去噪中的应用

阈值的选择是小波去噪的关键技术之一,但软硬阈值各有其缺陷.本文分析了自适应阈值的优点,进而提出逐点噪声方差法在去噪方面有更强的优势.仿真结果表明:采用自适应闭值并结合具有更强自适应性的逐点噪声方差法不仅能提高医学图像的峰值信噪比,还能有效地降低由传统阈值所带来的方块效应.     

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.     

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.     

计算机在超声医学图像处理中的应用

介绍了计算机在超声医学图像处理领域的应用,着重讨论了超声医学成像和超声医学图像管理的方法与应用,具体分析了医院医学图像存档及通信系统(PACS)中B超影像工作站的结构与功能。     

Robust non-homomorphic approach for speckle reduction in medical ultrasound images

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.     

Wavelet-based statistical approach for speckle reduction in medical ultrasound images

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Kσ/σ_x, where σ and σ_x were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.     

医学CT图像的几种后处理技术——VB6编程对CT影像的几种后处理

本文说明后处理的原理，并用VB6对五种主要的CT后处理技术编程，再应用于数字图形和医学CT影像，表现CT影像经后处理的特殊效果。     

Performance analysis of a new semiorthogonal spline wavelet compression algorithm for tonal medical images

Lossy image compression is thought to be a necessity as radiology moves toward a filmless environment. Compression algorithms based on the discrete cosine transform (DCT) are limited due to the infinite support of the cosine basis function. Wavelets, basis functions that have compact or nearly compact support, are mathematically better suited for decorrelating medical image data. A lossy compression algorithm based on semiorthogonal cubic spline wavelets has been implemented and tested on six different image modalities (magnetic resonance, x-ray computed tomography, single photon emission tomography, digital fluoroscopy, computed radiography, and ultrasound). The fidelity of the reconstructed wavelet images was compared to images compressed with a DCT algorithm for compression ratios of up to 40:1. The wavelet algorithm was found to have generally lower average error metrics and higher peak-signal-to-noise ratios than the DCT algorithm.     

Wavelet based compression of medical ultrasound images using vector quantization

In this paper, an efficient technique for compression of medical ultrasound (US) images is proposed. The technique is based on wavelet transform of the original image combined with vector quantization (VQ) of high-energy subbands using the LBG algorithm. First, we analyse the statistical behaviour of wavelet coefficients in US images across various subbands and scales. The analysis show that most of the image energy is concentrated in one of the detail subband, either in the vertical detail subband (most of the time) or in the horizontal subband. The other two subbands at each decomposition level contribute negligibly to the total image energy. Then, by exploiting this statistical analysis, a low-complexity image coder is designed, which applies VQ only to the highest energy subband while discarding the other detail subbands at each level of decomposition. The coder is tested on a series of abdominal and uterus greyscale US images. The experimental results indicate that the proposed method clearly outperforms the JPEG2000 (Joint Photographers Expert Group) encoder both qualitatively and quantitatively. For example, without using any entropy coder, the proposed method yields a peak signal to noise ratio gain of 0.2 dB to 1.2 dB over JPEG2000 on medical US images.     

基于小波分析的医学图像压缩技术进展

作为图像存诸和传输系统(picture archiving & communication system,PACS)的关键技术之一,医学图像压缩算法的优劣对PACS的性能有着重要的影响,小波分析由于其多分辨率分析特性而在医学图像压缩中得到了广泛应用.从小波变换医学图像压缩、小波包变换医学图像压缩和多小波变换医学图像压缩三个方面综述了小波医学图像压缩方法及其进展,总结对比了这些方法的优点和缺陷,并针对其不足之处提出了改进方向.     

Nonlinear analysis of the performance and reliability of wavelet singularity detection based denoising for Doppler ultrasound fetal heart rate signals

Many studies on the physiology of the cardiovascular system revealed that nonlinear chaotic dynamics govern the generation of the heart rate signal. This is also valid for the fetal heart rate (FHR) variability, where however the variability is affected by many more factors and is significantly more complicated than for the adult case. Recently an adaptive wavelet denoising method for the Doppler ultrasound FHR recordings has been introduced. In this paper the performance and reliability of that method is confirmed by the observation that for the wavelet denoised FHR signal, a deterministic nonlinear structure, which was concealed by the noise, becomes apparent. It provides strong evidence that the denoising process removes actual noise components and can therefore be utilized for the improvement of the signal quality. Hence by observing after denoising a significant improvement of the 'chaoticity' of the FHR signal we obtain strong evidence for the reliability and efficiency of the wavelet based denoising method. The estimation of the chaoticity of the FHR signal before and after the denoising is approached with three nonlinear analysis methods. First, the rescaled scale analysis (RSA) technique reveals that the denoising process increases the Hurst exponent parameter as happens when additive noise is removed from a chaotic signal. Second, the nonlinear prediction error evaluated with radial basis function (RBF) prediction networks is significantly lower at the denoised signal. The significant gain in predictability can be attributed to the drastic reduction of the additive noise from the signal by the denoising algorithm. Moreover, the evaluation of the correlation coefficient between actual and neural network predicted values as a function of the prediction time displays characteristics of chaos only for the denoised signal. Third, a chaotic attractor, reconstructed with the embedding dimension technique, becomes evident for the denoised signal, while it is completely obscured for the original signals. The correlation dimension of the reconstructed attractor for the denoised signal tends to reach a value independent of the embedding dimension, a sign of deterministic chaotic signal. In contrast for the original signal the correlation dimension increases steadily with the embedding dimension, a fact that indicates strong contribution of noise.     

Passive fetal monitoring by adaptive wavelet denoising method

Fetal Heart Rate (FHR) monitoring is one of the most important fetal well being tests. Existing FHR monitoring methods are based on Doppler ultrasound technique, which has several disadvantages. Passive fetal monitoring by phonocardiography is an appropriate alternative; however, its implementation is a challenging task due to low energy of fetal heart sounds and multiple interference signals presence. In this paper, an advanced signal processing method for passive fetal monitoring based on adaptive wavelet denoising is presented. The method's performance is compared with Doppler ultrasound monitor. The results show 94-97.5% accuracy, including highly disturbed cases.     

Investigation of a pulse compression technique for medical ultrasound: a simulation study

Pulse compression techniques that are capable of producing a large signal-to-noise (SNR) enhancement, have been used successfully in many different fields. For medical applications, frequency-dependent attenuation in soft tissue can limit the usefulness of this method. In the paper, this issue is examined through model-simulation studies. Frequency-modulation (FM) chirp, considered in the study, is just one form of pulse coding technique. Pulse propagation effects in soft tissue are modelled as a linear zero phase filter. A method to perform simulations and estimate the effective time-bandwidth product K is outlined. K describes the SNR enhancement attainable under limitations imposed by the soft-tissue medium. An effective time-bandwidth product is evaluated as a function of soft-tissue linear attenuation coefficient α_o, scatterer depth z and the bandwidth of the interrogating FM pulse, under realistic conditions. Results indicate that, under certain conditions, K can be significantly lower than its expected value in a non-attenuating medium. It is argued that although limitations exist, pulse compression techniques can still be used to improve resolution or increase penetrational depth. The real advantage over conventional short-pulse imaging comes from the possibility that these improvements can be accomplished without increasing the peak intensity of the interrogating pulse above any threshold levels set by possible bio-effect considerations.     

Comparative study of wavelet denoising in myoelectric control applications

Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.     

最优改进对数谱幅度估计与小波结合的心音降噪

小波阈值降噪为心音降噪的一种常用方法。本文提出了使用最优改进对数幅度谱估计与小波阈值降噪相结合的方法对心音降噪。在正常心音和一些常见疾病的心音中加入不同强度的白噪声和粉红噪声，构造不同信噪比的心音信号，并将本文所提出的方法和仅用小波阈值降噪方法的去噪效果进行了定量的对比。结果表明本文方法降噪效果总体优于仅使用小波阈值降噪达到的效果。