Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution

The authors present a statistical approach to speckle reduction in medical ultrasound B-scan images based on maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of statistical model for speckle noise is proposed to obtain a simple and tractable solution in a closed analytical form. The proposed method uses the Rayleigh distribution for speckle noise and a Gaussian distribution for modelling the statistics of wavelet coefficients in a logarithmically transformed ultrasound image. The method combines the MAP estimation with the assumption that speckle is spatially correlated within a small window and designs a locally adaptive Bayesian processor whose parameters are computed from the neighboring coefficients. Further, the locally adaptive estimator is extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. The experimental results show that the proposed method clearly outperforms the state-of-the-art medical image denoising algorithm of Pizurica et al., spatially adaptive single-resolution methods and band-adaptive multi-scale soft-thresholding techniques in terms of quantitative performance as well as in terms of visual quality of the images. The main advantage of the new method over the existing techniques is that it suppresses speckle noise well, while retaining the structure of the image, particularly the thin bright streaks, which tend to occur along boundaries between tissue layers.     

A novel approach to the 2-D blind deconvolution problem in medical ultrasound

The finite frequency bandwidth of ultrasound transducers and the nonnegligible width of transmitted acoustic beams are the most significant factors that limit the resolution of medical ultrasound imaging. Consequently, in order to recover diagnostically important image details, obscured due to the resolution limitations, an image restoration procedure should be applied. The present study addresses the problem of ultrasound image restoration by means of the blind-deconvolution techniques. Given an acquired ultrasound image, algorithms of this kind perform either concurrent or successive estimation of the point-spread function (PSF) of the imaging system and the original image. In this paper, a blind-deconvolution algorithm is proposed, in which the PSF is recovered as a preliminary stage of the restoration problem. As the accuracy of this estimation affects all the following stages of the image restoration, it is considered as the most fundamental and important problem. The contribution of the present study is twofold. First, it introduces a novel approach to the problem of estimating the PSF, which is based on a generalization of several fundamental concepts of the homomorphic deconvolution. It is shown that a useful estimate of the spectrum of the PSF can be obtained by applying a proper smoothing operator to both log-magnitude and phase of the spectra of acquired radio-frequency (RF) images. It is demonstrated that the proposed approach performs considerably better than the existing homomorphic (cepstrum-based) deconvolution methods. Second, the study shows that given a reliable estimate of the PSF, it is possible to deconvolve it out of the RF-image and obtain an estimate of the true tissue reflectivity function, which is relatively independent of the properties of the imaging system. The deconvolution was performed using the maximum a-posteriori (MAP) estimation framework for a number of statistical priors assumed for the reflectivity function. It is shown in a series of in vivo experiments that reconstructions based on the priors, which tend to emphasize the "sparseness" of the tissue structure, result in solutions of higher resolution and contrast.     

Nonlocal Means-Based Speckle Filtering for Ultrasound Images

In image processing, restoration is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. In this paper, an adaptation of the nonlocal (NL)-means filter is proposed for speckle reduction in ultrasound (US) images. Originally developed for additive white Gaussian noise, we propose to use a Bayesian framework to derive a NL-means filter adapted to a relevant ultrasound noise model. Quantitative results on synthetic data show the performances of the proposed method compared to well-established and state-of-the-art methods. Results on real images demonstrate that the proposed method is able to preserve accurately edges and structural details of the image.     

Wavelet-based space-frequency compression of ultrasound images

This paper describes the compression of grayscale medical ultrasound images using a recent compression technique, i.e., space-frequency segmentation (SITS). This method finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations and is especially effective when the images to code are statistically inhomogeneous, which is the case for medical ultrasound images. We implemented a compression application based on this method and tested the algorithm on representative ultrasound images. The result is an effective technique that performs better than a leading wavelet-transform coding algorithm, i.e., set partitioning in hierarchical trees (SPIHT), using standard objective distortion measures. To determine the subjective qualitative performance, an expert viewer study was run by presenting ultrasound radiologists with images compressed using both SFS and SPIHT. The results confirmed the objective performance rankings. Finally, the performance sensitivity of the space-frequency codec is shown with respect to several parameters, and the characteristic space-frequency partitions found for ultrasound images are discussed     

结合稀疏表示与匹配梯度分布的图像复原

针对基于稀疏表示的传统图像复原方法无法准确恢 复图像小尺度细节的不足,提出了一种结合稀疏 表示与匹配梯度分布的图像复原方法。首先在稀疏表示图像复原模型的基础上,利用参数化 的超拉 普拉斯分布估计原始图像的梯度分布;然后,通过对图像的梯度分布进行全局约束,利用梯 度直方图匹配 操作匹配图像梯度分布,使复原图像的梯度分布尽可能接近原始图像。仿真实验结果表明 , 本文算法能够取得较优的复原效果, 并且能以较高精度复原图像的细节信息。     

Boundary enhancement and speckle reduction for ultrasound images via salient structure extraction

In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.     

Image Deblurring Via Combined Total Variation and Framelet

This paper presents a new approach to image deblurring, on the basis of total variation (TV) and wavelet frame. The Rudin–Osher–Fatemi model, which is based on TV minimization, has been proven effective for image restoration. The explicit exploitation of sparse approximations of natural images has led to the success of wavelet frame approach in solving image restoration problems. However, TV introduces staircase effects. Thus, we propose a new objective functional that combines the tight wavelet frame and TV to reconstruct images from blurry and noisy observations while mitigating staircase effects. The minimization of the new objective functional presents a computational challenge. We propose a fast minimization algorithm by employing the augmented Lagrangian technique. The experiments on a set of image deblurring benchmark problems show that the proposed method outperforms the previous state-of-the-art methods for image restoration.     

Modeling the amplitude statistics of ultrasonic images

In this paper, a new statistical model for representing the amplitude statistics of ultrasonic images is presented. The model is called the Rician inverse Gaussian (RiIG) distribution, due to the fact that it is constructed as a mixture of the Rice distribution and the Inverse Gaussian distribution. The probability density function (pdf) of the RiIG model is given in closed form as a function of three parameters. Some theoretical background on this new model is discussed, and an iterative algorithm for estimating its parameters from data is given. Then, the appropriateness of the RiIG distribution as a model for the amplitude statistics of medical ultrasound images is experimentally studied. It is shown that the new distribution can fit to the various shapes of local histograms of linearly scaled ultrasound data better than existing models. A log-likelihood cross-validation comparison of the predictive performance of the RiIG, the K, and the generalized Nakagami models turns out in favor of the new model. Furthermore, a maximum a posteriori (MAP) filter is developed based on the RiIG distribution. Experimental studies show that the RiIG MAP filter has excellent filtering performance in the sense that it smooths homogeneous regions, and at the same time preserves details.     

基于Poisson-Markov场的超分辨力图像复原算法

图像的超分辨力复原和信噪比的提高是图像复原追求的目标.Poisson-ML图像复原方法(PML)具有很强的超分辨力复原能力,但在复原过程中会产生振荡条纹且对带噪较大的图像不能取得理想的复原效果.在Poisson和Markov分布假设的基础上,提出基于Poisson-Markov场的超分辨力图像复原算法及其正则化参数的自适应选择方法(MPML).实验表明,MPML算法不但具有很好的超分辨力复原能力,而且能有效减少和去除复原图像中的振荡条纹,对于带噪较大的图像也能取得理想的复原效果,因此其图像复原质量明显好于PML算法.正则化参数能被自动优化地选择且与图像复原的迭代运算同步进行.     

运动模糊图像复原算法的改进及性能研究*

为提高运动模糊图像复原算法的有效性和实时性,分析了常见图像复原算法的优缺点。在此基础上,提出了一种基于小波分解和维纳滤波相结合的图像复原算法。新算法充分利用了小波变换的多分辨率分析特性和维纳滤波复原算法的高效性,既有效抑制了噪声,又减小了图像的灰度失真。仿真结果表明,本文算法不仅提升了图像复原质量,也能满足系统实时性的要求,是一种有效的图像复原算法。     

边缘检测算法在医学超声液性病变图像中的应用

医学超声液性病变图像多见数个无回声区,呈"蜂窝状",边界不清晰,为了清晰地提取医学超声液性病变图像的边缘,进一步为临床诊断提供可靠依据,在此将几种不同的边缘检测算法应用于医学超声液态病变图像中,经实验结果得出,经典的边缘检测算法不能很好地提取图像的边缘,而基于Snake模型的边缘检测算法,人为设定边缘控制点,智能动态调整曲线,获得了很好的边缘提取效果,具有很高的临床应用价值。     

一种改进的全变差盲图像复原方法

当点传播函数未知或不确知的情况下,从观察到的退化图像中复原原始图像的过程称为图像盲复原.传统的图像盲复原算法常采用最小均方误差作为复原效果的评判准则,但它很少考虑人类视觉心理,而图像最终都必须由人类的视觉系统来观测和解释.因此,本文提出一种新的基于人类视觉特性的图像盲复原算法:它采用交替最小化的结构,在模糊辨识阶段,采用全变差正则化算法;在复原阶段,采用基于Weber定律和全变差正则化相结合的算法.仿真实验表明,这种算法可在未知点扩展函数的情况下取得较好的复原效果.     

二阶Hopfield型神经网络的稳定性 分析及收敛速度的估计

本文讨论二阶连续Hopfield型神经网络平衡点的全局稳定性问题,利用LMI方法和Lyapunov方法得到了网络平衡点全局渐近稳定和全局指数稳定的几个充分条件,并对其指数收敛速度进行了估计.     

基于尺度旋转的图像恢复研究

运动模糊图像恢复是图像处理中的重要部分。本文将计算机图形学尺度旋转引入运动模糊图像恢复中,提出基于尺度旋转变换的运动模糊图像自动搜索恢复模型,并阐述建立模型的完整过程和均方误差准则下自动搜索优化算法。实验表明,模型较好解决了传统运动模糊图像恢复中无参数恢复图像的缺陷,恢复效果良好,是一种有效的图像恢复方法。     

医学图像增强系统设计

为了改善医学图像的视觉效果,提高图像的清晰度,使之更适合于机器的分析处理以及人的视觉特性,并突出病灶点,为病理学诊断和临床诊断提供可靠依据。设计了一个对医学图像十分具有针对性的图像增强系统。针对CT图像的电子噪声提出了基于修正维纳滤波的小波包去噪算法;针对B型超声图像的散斑噪声提出了基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法;针对医学图像对比度低,边缘信息模糊等特点,提出了基于小波变换的医学图像增强算法。当噪声方差为0.01时,基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法获得的PSNR比经Wiener滤波方法获得的PSNR高出9 dB。系统能快速找到噪声点进行定点去噪,能有效提高医学图像的对比度,增强边缘细节信息,突出病灶点的位置,从而达到较好的处理效果,为医疗工作者观察病症提供更加清晰准确的依据。     

Regularised restoration of vector quantisation compressed images

The authors study the application of image restoration technology in improving the coding performance of a vector quantisation (VQ) image compression codec. Restoration of VQ-compressed images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. A restoration algorithm is proposed, specific to VQ-compressed images, that makes good use of the codebook to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a VQ-compressed image to a much greater extent, compared with other existing restoration approaches. As no extra information, other than the codebook, is required to carry out the restoration with the proposed algorithm, no transmission overhead is necessary and hence, it can be fully compatible with any VQ codec when used to improve coding performance     

应用改进自蛇模型和L-R算法恢复毫米波图像

在毫米波的图像恢复中,L-R算法是一种简单而有效的非线性方法,但当噪声不可忽略时,L-R算法难以获得较好的复原结果。针对毫米波图像数据量少和图像分辨率低的特点,提出基于改进自蛇模型和L-R算法毫米波图像恢复方法,以局部方差构造自蛇模型的边缘停止函数,其改进自蛇模型在消除噪声的同时更能够保留图像中的边缘和细节特征,然后使用L-R算法进行图像恢复,这种改进算法通过使用基于改进自蛇模型去噪能有效地减少噪声对L-R算法的影响。实验结果表明:在信噪比和相关度方面本文算法提高了L-R算法的性能,可用于含噪声的图像复原。     

交替使用小波去噪和全变差正则化的盲图像恢复算法

盲图像恢复就是在点扩散函数未知情况下从降质观测图像恢复出原图像.该文提出了一种交替使用小波去噪和全变差正则化的盲图像恢复算法.观测模型首先被分解成两个相互关联的子模型,这种分解转化盲恢复问题成为图像去噪和图像恢复两个问题,可以交替采用图像去噪和图像恢复算法求解.模糊辨识阶段,使用全变差正则化算法估计点扩散函数;图像恢复阶段,使用小波去噪和全变差正则化相结合的算法恢复图像.实验结果和与其它方法的比较表明该文算法能够获得更好的恢复效果.     

基于小波变换的高分辨率遥感图像复原算法实现

为了促进遥感图像的后续研究,针对高分辨率遥感图像实现了基于小波变换的迭代收缩（IST）图像复原算法。考虑到算法在复原过程中对内存需求较大,实现过程中采用内存映射文件的方法,将高分辨率遥感图像映射到进程地址空间。针对分块复原图像时通常会伴有边缘跳变现象,影响拼接后的图像质量的问题,使用特殊分块策略对图像进行分块处理。复原算法在VC平台下实现,通过遥感图像复原实验,并对复原图像进行评价分析,复原性能和效率良好。     

基于灰度和边界方向直方图的医学图像检索

本文研究了采用分级检索的机制,综合利用灰度及形状特征进行基于内容的医学图像检索的方法,该方法克服了灰度直方图不能充分表示空间分布信息的不足。利用边界方向直方图描述形状特征,避开了对图像进行精确分割这一医学图像处理中的难点问题。对CT图像数据库进行的检索实验,验证了该方法具有良好的检索性能。