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。     

基于对象识别和分割的图像噪声抑制

在医学图像降噪中,不损失图像细节是至关重要的.传统的方法是直接对整个图像进行滤波处理.然而这种操作在减少噪声的同时也会破坏图像的细节.因此,关键的问题就是怎样在保持图像细节的同时能够减少噪声.为了解决上面提到的问题,在这篇论文中提出了一种模拟生物视觉的图像处理方法,首先提取图像的边缘信息,将图像分割成边缘区域和非边缘区域,然后对这两个区域采取不同的滤波降噪,再进行重新合成.与传统方法相比,本方法在保存图像细节上有一定的效果.     

比较二维小波和二维小波包技术在不同压缩模式下的降噪处理

背景:小波和小波包技术是进行时频信号分析的重要方法.医学图像数字化采集后断层多,数据信息量大,易受噪声影响.采用二维小波技术和小波包技术可以实现肝癌图像的完美压缩和降噪.目的:比较二维小波和二维小波包技术在不同压缩模式下压缩肝癌图像的优劣以及小波降噪的技巧.方法:选用同一幅动脉期肝癌图像,进行4层分解,利用二维小波和二维小波包技术分别进行Balance sparsity-norm、Remove nearO和Bal.sparsity-norm(sqrt)三种模式的压缩处理,再利用小波函数对含噪声信号的图像进行降噪处理.结果与结论:对于同一种压缩模式,二维小波包技术压缩肝癌图像优于二维小波技术,3种压缩模式中Bal.sparsity-norm(sqrt)模式和Remove nearO mode模式压缩比例更小,图像清晰度更好;小波降噪能很好地消除噪声信号.提示利用二维小波技术和小波包技术都可以实现肝癌图像的完美压缩和降噪.     

基于小波极大模值信息的医学图像去噪方法

目的 利用小波变换进行医学图像去噪.方法 通过分析二进小波变换下小波极大模值的特点,即信号的极大模值往往会大于噪声的极大模值,而且噪声的极大模值会随着尺度增大而急剧减少,信号的极大模值却改变很小,由此构造了更有效的去噪准则,即根据不同尺度上的极大模值信息,选择不同的域值来滤除噪声.结果 应用该方法进行医学图像去噪,能保持较高的峰值信噪比、图像细节和边缘特征以及图像清晰度.结论 基于小波极大模值信息的去噪方法能有效地降低医学图像中的噪声.     

基于灰度对比和自适应小波变换的X射线图像增强

背景:X射线检查作为常规的检查方式得到了广泛的应用,然而由于现有技术的局限性,使得X射线图像往往具有灰度对比度低和噪声影响等缺点,因此,现有的X射线图像往往达不到医生的要求.目的:增强和去噪处理对比度较低且含有噪声的X射线图像,以达到易于医生理解和识别的目的.方法:针对空间域处理和变换域处理增强X射线图像的不足,提出了基于灰度对比和自适应小波变换的X射线图像增强算法.首先,选择需要增强和减弱的灰度范围,并根据八邻域灰度对比增强算法对X射线图像进行灰度变换,并用中值滤波算法对图像进行平滑;然后,对X射线图像进行小波分解,并运用相邻分解层之间相关系数的大小来确定细节信号和噪声.结果与结论:应用了灰度对比和自适应小波变换相结合的X射线图像增强算法,把基于空间域增强的方法和基于变换域的方法有机地结合起来,比传统的单一增强方法更为优越.实验结果证明它能自适应地增强X射线图像的灰度对比,使得图像细节的显示更加清晰,同时在一定程度上去除了噪声的干扰,对于灰度对比度较低的图像效果更加明显.     

小波域Wiener滤波方法及其在功能磁共振成像中的应用

目的:探讨基于多分辨率分析理论的小波域Wiener滤波方法,并观察其在功能磁共振数据分析中的降噪效果。方法:①小波域Wiener滤波方法介绍:时间域高分辨率功能磁共振图像常常带有大量噪声,并且其分布是不确定的,基于多分辨率分析理论的小波域Wiener滤波将不确定分布的一般噪声转换到小波域相互正交的不同尺度空间上,使其在同一尺度空间上为互不相关的附加高斯分布。应用小波削弱算法Wiener滤波分别在不同尺度空间上进行降噪,同时对同一尺度空间信号使用了稳健中位数法进行估计。②应用该方法分别对模拟数据(一方波加上随机噪声,主要是模拟实际的功能磁共振方波)和真实数据进行分析及处理。结果:①模拟数据去噪前、后信噪比分别为68.987,78.529,表明降噪后信噪比明显提高;加噪声的混合数据、经小波处理后的数据与未加噪声的原数据很接近,附加的噪声得到了明显的抑制。②真实数据计算结果表明,经小波域Wiener滤波降噪以后,提高了数据的信噪比,从而能够更有效地提取信号,有助于识别新的有效激活区或排除伪激活区。结论:在功能磁共振数据分析中应用小波域Wiener滤波方法降噪是有效的。     

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.     

小波先验在OSL重建算法中的应用

背景:MAP(最大后验)统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的.目的:根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声.方法:实验从“变换域"的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建.结果与结论:基于小波先验的OSL算法比ML-EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚.     

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.     

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.     

Speckles Suppression Techniques for Ultrasound Images

Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average—0.4 dB higher PSNR—than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.     

k-means based hybrid wavelet and curvelet transform approach for denoising of remotely sensed images

This article presents a new technique for denoising of remotely sensed images based on multi-resolution analysis (MRA). Multi-resolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image processing and analysis. The multi-resolution image analysis methods have the ability to analyse the image in an adaptive manner, capturing local as well as global information. Further, noise, as one of the biggest obstacles for image analysis and for further processing, is effectively handled by multi-resolution methods. The article aims at the analysis of noise filtering of image using wavelets and curvelets methods on multispectral images acquired by the QuickBird and medium-resolution Landsat Thematic Mapper satellite systems. To improve the performance of noise filtering, an iterative thresholding scheme and a hybrid approach based on wavelet and curvelet transforms are proposed for restoring the image from its noisy version. Two comparative measures are used for evaluation of the performance of the methods for denoising. One of them is the peak signal-to-noise ratio and the second is the ability of the noise filtering scheme to preserve the sharpness of the edges. By both of these comparative measures, the hybrid approach of curvelet and wavelet for heterogeneous and homogeneous areas with iterative threshold has proved to be better than the others. Results are illustrated using QuickBird and Landsat images for proposed methods and compared with wavelets and curvelet-based denoising.     

基于超声图像边缘的乳腺肿瘤良恶性判别

目的：提取乳腺肿瘤超声图像的边缘,判别乳腺肿瘤的良恶性。方法：提出了一种改进的各向异性扩散滤波,即根据图像不同的梯度选择不同的扩散系数的扩散方程,滤除噪声的同时很好地保留了图像的边缘信息。然后在Level Set方法中提出一种新的变分公式,完全避免了重初始化步骤,且水平集函数的初始化灵活,采用手工勾画粗略边界的半自动分割流程,不仅提高了分割准确性,同时也进一步提高了分割效率。结果：采用综合灰度共生矩阵计算乳腺肿瘤的纹理特征和模糊C均值的方法进行良恶性判别,正确率为72.64%。结论：实验证明,本文算法能高效准确地提取出肿瘤边界,为肿瘤良恶性的判别提供可靠的依据。     

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.     

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     

Speckle noise reduction in ultrasound biomedical B-scan images using discrete topological derivative

Over three decades, several despeckling techniques have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information. The topological derivative (TD) is the recently adopted technique in the area of biomedical image processing. In this work, we computed the topological derivative for an appropriate function associated to the ultrasound B-scan image gradient by assigning a diffusion factor k, which indicates the cost endowed to that particular image. In this article, a novel image denoising approach, called discrete topological derivative (DTD) has been implemented. The algorithm has been developed in MATLAB7.1 and tested over 200 ultrasound B-scan images of several organs such as the liver, kidney, gall bladder and pancreas. Further, the performance of the DTD algorithm has been estimated by calculating important performance metrics. A comparative study was carried out between the DTD and the traditional despeckling techniques. The calculated peak signal-to-noise ratio (PSNR) (the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation) value of the DTD despeckled liver image is found to be 28 which is comparable with the outperformed speckle reducing anisotropic diffusion (SRAD) filter. SRAD filter is an edge-sensitive diffusion method for speckled images of ultrasonic and radar imaging applications. Canny edge detection and visual inspection of DTD filtered images by the trained radiologist found that the DTD algorithm preserves the hypoechoic and hyperechoic regions resulting in improved diagnosis as well as tissue characterization.     

Breast Ultrasound Despeckling Using Anisotropic Diffusion Guided by Texture Descriptors

Breast ultrasound (BUS) is considered the most important adjunct method to mammography for diagnosing cancer. However, this image modality suffers from an intrinsic artifact called speckle noise, which degrades spatial and contrast resolution and obscures the screened anatomy. Hence, it is necessary to reduce speckle artifacts before performing image analysis by means of computer-aided diagnosis systems, for example. In addition, the trade-off between smoothing level and preservation of lesion contour details should be addressed by speckle reduction schemes. In this scenario, we propose a BUS despeckling method based on anisotropic diffusion guided by Log–Gabor filters (ADLG). Because we assume that different breast tissues have distinct textures, in our approach we perform a multichannel decomposition of the BUS image using Log–Gabor filters. Next, the conduction coefficient of anisotropic diffusion filtering is computed using texture responses instead of intensity values as stated originally. The proposed algorithm is validated using both synthetic and real breast data sets, with 900 and 50 images, respectively. The performance measures are compared with four existing speckle reduction schemes based on anisotropic diffusion: conventional anisotropic diffusion filtering (CADF), speckle-reducing anisotropic diffusion (SRAD), texture-oriented anisotropic diffusion (TOAD), and interference-based speckle filtering followed by anisotropic diffusion (ISFAD). The validity metrics are the Pratt’s figure of merit, for synthetic images, and the mean radial distance (in pixels), for real sonographies. Figure of merit and mean radial distance indices should tend toward ‘1’ and ‘0’, respectively, to indicate adequate edge preservation. The results suggest that ADLG outperforms the four speckle removal filters compared with respect to simulated and real BUS images. For each method—ADLG, CADF, SRAD, TOAD and ISFAD—the figure of merit median values are 0.83, 0.40, 0.39, 0.51 and 0.59, and the mean radial distance median results are 4.19, 6.29, 6.39, 6.43 and 5.88.     

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.