轮廓波及曲波和小波变换用于显微图像消噪的比较

背景:小波变换只能反映信号的零维奇异性,无法最优表示图像中的线奇异;而且小波变换只存在3个方向,这些都显著影响了它在图像处理领域的应用效果.针对小波变换的缺点,多尺度几何分析理论正在逐步发展,轮廓波变换和曲波变换就是其中的典型代表.目的:定性、定量地比较轮廓波、曲波和小波变换在图像消噪处理中的效果.方法:在简要介绍3种变换基本原理的基础上,比较它们在图像消噪领域的应用,以均方误差和峰值信噪比作为定量指标评价消噪效果,并将其应用于显微镜图像的消噪处理.结果与结论:综合定量评价指标和人眼视觉感受,曲波变换的消噪结果最佳,轮廓波变换效果次之,小波变换效果则不够理想.     

轮廓波及曲波和小波变换用于显微图像消噪的比较

背景：小波变换只能反映信号的零维奇异性,无法最优表示图像中的线奇异;而且小波变换只存在3个方向,这些都显著影响了它在图像处理领域的应用效果。针对小波变换的缺点,多尺度几何分析理论正在逐步发展,轮廓波变换和曲波变换就是其中的典型代表。目的：定性、定量地比较轮廓波、曲波和小波变换在图像消噪处理中的效果。方法：在简要介绍3种变换基本原理的基础上,比较它们在图像消噪领域的应用,以均方误差和峰值信噪比作为定量指标评价消噪效果,并将其应用于显微镜图像的消噪处理。结果与结论：综合定量评价指标和人眼视觉感受,曲波变换的消噪结果最佳,轮廓波变换效果次之,小波变换效果则不够理想。     

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     

Adaptive superpixel based Markov random field model for unsupervised change detection using remotely sensed images

This study presents an adaptive superpixel based Markov Random Field (ASP_MRF) model for unsupervised remotely sensed images change detection. Firstly, the difference image is generated by change vector analysis (CVA) and the zero parameter version of the ‘simple linear iterative clustering’ method (SLICO) is applied on the difference image to obtain the superpixel map. Then, the superpixel map is initially labeled as changed and unchanged class by Fuzzy c-means (FCM) clustering method. Thirdly, the region adjacent graph (RAG) is built on the superpixel map to model the spatial constraints between the adjacent superpixels. Specially, the spectral dissimilarity between the adjacent superpixels and the label fuzziness of the neighbored superpixels were incorporated in the RAG. Lastly, The initial labels of the superpixel map are iteratively refined with ASP_MRF to generate the final change map. The experimental results prove that ASP_MRF obtained the most accurate change map and outperformed the results by pixel level MRF and superpixel based MRF, which verifies the effectiveness of ASP_MRF.     

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.     

Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images

In this paper, a novel change detection approach is proposed using fuzzy c-means (FCM) and Markov random field (MRF). First, the initial change map and cluster (changed and unchanged) membership probability are generated through applying FCM to the difference image created by change vector analysis (CVA) method. Then, to reduce the over-smooth results in the traditional MRF, the spatial attraction model is integrated into the MRF to refine the initial change map. The adaptive weight is computed based on the cluster membership and distances between the centre pixel and its neighbourhood pixels instead of the equivalent value of the traditional MRF using the spatial attraction model. Finally, the refined change map is produced through the improved MRF model. Two experiments were carried and compared with some state-of-the-art unsupervised change detection methods to evaluate the effectiveness of the proposed approach. Experimental results indicate that FCMMRF obtains the highest accuracy among methods used in this paper, which confirms its effectiveness to change detection.     

A novel dynamic threshold method for unsupervised change detection from remotely sensed images

In this letter, a dynamic threshold method is proposed for unsupervised change detection from remotely sensed images. First, change vector analysis technique is applied to generate the difference image. Then the statistical parameters of the difference image are estimated by Expectation Maximum algorithm assuming that the change and no-change pixel sets are modelled by Gaussian Mixture Model. As a result, a global initial threshold can be identified based on Bayesian decision theory. Next, a dynamic threshold operator is proposed by incorporating the membership value of each pixel generated by the Fuzzy c-means (FCM) algorithm and the global initial threshold. Lastly, the change map is obtained by segmenting the difference image utilizing the dynamic threshold proposed. Experimental results indicate that the proposed dynamic threshold method has significantly reduced the speckle noise comparing to the global threshold method. At the same time, weak change signals are detected and detail change information are preserved much better than the FCM does.     

A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images

Denoising and contrast enhancement play key roles in optimizing the trade-off between image quality and X-ray dose. However, these tasks present multiple challenges raised by noise level, low visibility of fine anatomical structures, heterogeneous conditions due to different exposure parameters, and patient characteristics. This work proposes a new method to address these challenges. We first introduce a patch-based filter adapted to the properties of the noise corrupting X-ray images. The filtered images are then used as oracles to define non parametric noise containment maps that, when applied in a multiscale contrast enhancement framework, allow optimizing the trade-off between improvement of the visibility of anatomical structures and noise reduction. A significant amount of tests on both phantoms and clinical images has shown that the proposed method is better suited than others for visual inspection for diagnosis, even when compared to an algorithm used to process low dose images in clinical routine.     

诱发电位提取的子空间和小波去噪复合方法

目的:为减少提取诱发电位所需的试验次数,有效去除自发脑电噪声,提出一种新的视觉诱发电位提取方法并进行验证。方法:基于奇异值分解的子空间方法可以用于去除信号中的噪声。①其基本原理是,由含噪信号形成的数据矩阵进行奇异值分解可以获得信号子空间和噪声子空间,将含噪信号正交投影到信号子空间中即可得到去除噪声。因为在头皮测量得到的诱发电位记录信号的信噪比很低,所以仅使用基于奇异值分解的子空间方法来去除噪声并不能有效地提取诱发电位。②实验记录中对诱发电位成分影响较大的自发脑电是有色噪声,描述其奇异性的Lipschitz指数具有不确定性,可能为正,也可能为负,因此仅用小波去噪方法提取诱发电位也不能取得理想的结果。③为此,提出了一种基于奇异值分解的子空间正交投影和小波去噪复合方法来提取诱发电位。首先应用基于奇异值分解的子空间方法将包含噪声的记录信号分解为信号子空间和噪声子空间,将含噪信号投影到信号子空间可得到初步去噪的信号,再应用小波变换进一步去除噪声,即可提取诱发电位。结果:采用自发脑电模型产生有色的自发脑电噪声,与白噪声一起加入仿真的诱发脑电信号中,在低信噪比小于-10dB的情况下,可有效地提取出诱发脑电信号。仿真和实验结果表明这种复合方法的效果好于单独采用其中的一种方法,能将提取诱发电位的实验次数由20次左右缩短为四五次。结论:将基于奇异值分解的子空间方法和小波去噪结合起来,能有效提取诱发电位,减少提取诱发电位所需的试验次数。     

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.     

Sparsity based denoising of spectral domain optical coherence tomography images

In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority of the MSBTD algorithm compared to popular denoising algorithms on images from normal and age-related macular degeneration eyes of a multi-center clinical trial. We have made the corresponding data set and software freely available online.     

A hybrid approach for segmentation of anatomic structures in medical images

Objective

We propose a hybrid interactive approach for the segmentation of anatomic structures in medical images with higher accuracy at lower user interaction cost.

Materials and methods

Eighteen brain MR scans from the Internet Brain Segmentation Repository are used for brain structure segmentation. A MR scan and a CT scan of an old female are used for orbital structure segmentation. The proposed approach combines shape-based interpolation, radial basis function (RBF)-based warping and model-based segmentation. With this approach, to segment a structure in a 3D image, we first delineate the structure in several slices using interactive methods, and then use shape-based interpolation to automatically generate an initial 3D model of the structure from the segmented slices. To refine the initial model, we specify a set of additional points on the structure boundary in the image, and use a RBF to warp the model so that it passes the specified points. Finally, we adopt a point-anchored active surface approach to further deform the model for a better fitting of the model with its corresponding structure in image.

Results

Two brain structures and 15 orbital structures are segmented. For each structure, it needs only to semi- automatically segment three to five 2D slices and specify two to nine additional points on the structure boundary. The time cost for each structure is about 1–3 min. The overlap ratio of the segmentation results and the ground truth is higher than 96%.

Conclusion

The proposed method for the segmentation of anatomic structure achieved higher accuracy at lower user interaction cost, and therefore promising in many applications such as surgery planning and simulation, atlas construction, and morphometric analysis of anatomic structures.     

基于小波变换的心音包络提取算法及应用

背景：心音信号包含了大量心脏瓣膜活动的生理信息,心音分析对诊断心脏疾病具有重要的临床意义。目的：旨在通过心音的包络提取,分析心音信号的各种特征,进而判断心音中是否包含杂音,以改善传统听诊技术高度依赖医生经验、听诊范围受限的缺点。方法：提出了一种采用小波变换来提取心音包络的方法,通过与采用希尔伯特-黄变换、数学形态学、平均香农能量等心音包络求解方法进行对比,证明这种方法具有算法简便、曲线光滑、特征点突出等优点。结果与结论：将该方法用于临床真实心音的包络提取,利用支持向量机来训练所提取心音包络的面积和小波能量两个特征参数,判别心音信号是否明显包含杂音。选用35例心音数据对算法进行验证,结果表明该算法的准确率达到95%,具有很强的实用性。     

基于小波变换的心音包络提取算法及应用

背景:心音信号包含了大量心脏瓣膜活动的生理信息,心音分析对诊断心脏疾病具有重要的临床意义。目的:旨在通过心音的包络提取,分析心音信号的各种特征,进而判断心音中是否包含杂音,以改善传统听诊技术高度依赖医生经验、听诊范围受限的缺点。方法:提出了一种采用小波变换来提取心音包络的方法,通过与采用希尔伯特-黄变换、数学形态学、平均香农能量等心音包络求解方法进行对比,证明这种方法具有算法简便、曲线光滑、特征点突出等优点。结果与结论:将该方法用于临床真实心音的包络提取,利用支持向量机来训练所提取心音包络的面积和小波能量两个特征参数,判别心音信号是否明显包含杂音。选用35例心音数据对算法进行验证,结果表明该算法的准确率达到95%,具有很强的实用性。     

基于Gabor小波变换与灰度相结合技术设计的人眼检测

随着计算机技术、现代通讯技术、信息技术的进步,中国的远程诊疗事业进入了一个高速发展期,远程诊疗以新的方式给患者带来了安全而经济的医疗保健服务.基于Internet的远程医疗系统打破了传统医疗在时间、地点和形式上的重重羁绊,实现了医疗数据和资源共享与远程专家会诊.基于眼睛分析的中医远程诊疗系统的实现,人眼定位是一个很重要、很有挑战性的任务.作者提出了基于Gabor小波变换算法和灰度的人眼检测算法,通过对眼睛部分的灰度复杂度计算来对眼睛进行初步定位,利用Gabor小波在粗略区域精确定位.该算法简单实用快速,实验结果表明该定位方法在标准人脸数据集上取得了很好的效果,在困难人脸图像上同样显示了很好的鲁棒性.     

基于多通道肌电信号小波变换的人手运动识别

目的：提出一种基于多通道肌电信号小波变换提取人手多种运动模式的方法。方法：对人手上肢肌肉记录的表面肌电信号进行多尺度分解，利用肌电信号小波系数的方差构造特征空间。然后从这个特征空间选择指定动作对应的两块肌肉的小波系数方差，求得两者间的比值。结果和结论：构造了新的特征空间，这个空间中包含了可识别各种人手动作的特征值，这种方法可以用于人手动作的识别，为研究多自由度假手表面肌电信号控制方法提供了新途径。     

Change detection in SAR images based on the logarithmic transformation and total variation denoising method

Noise interference and the need to process massive image data present challenges to change detection in synthetic aperture radar (SAR) images. In order to improve the change detection accuracy and decrease the processing time, this paper proposes a novel unsupervised change detection algorithm for SAR images. The logarithmic transformation is applied to transform images into the logarithmic domain, while the multiplicative noise in images is transformed into additive noise. The total variation (TV) denoising algorithm is then used to reduce image noise, and the difference operator in the logarithmic domain is employed to provide the difference image. The k-means clustering algorithm, which does not require consideration of the statistical properties of an image, is employed to cluster the difference image into two disjointed classes: changed and unchanged. The experimental results demonstrate that change detection results achieved by the proposed algorithm offer great improvement over existing algorithms in terms of objective quantitative indices and the visual effect.     

SAR image denoising in nonsubsampled contourlet transform domain based on maximum a posteriori and non-local constraint

An approach of synthetic aperture radar (SAR) image denoising in nonsubsampled contourlet transform (NSCT) domain based on maximum a posteriori (MAP) and non-local (N-L) constraint is proposed. SAR image is firstly modelled by a nonlogarithmic additive model for modelling of the speckle in NSCT domain. Then, coefficients of real signals are obtained in the NSCT domain with MAP adaptive shrinkage. As it tends to eliminate too many coefficients that contain useful information by shrinkage, the N-L constraint is introduced to smooth the coefficients left in each subband, for each pixel in the subbands of NSCT corresponding to those in the same location of the original image. Experiments show that the proposed approach is effective in SAR image denoising and texture preserving, in comparison with some traditional algorithms.     

Multi-scale classification of disease using structural MRI and wavelet transform

Recently, multivariate analysis algorithms have become a popular tool to diagnose neurological diseases based on neuroimaging data. Most studies, however, are biased for one specific scale, namely the scale given by the spatial resolution (i.e. dimension) of the data. In the present study, we propose to use the dual-tree complex wavelet transform to extract information on different spatial scales from structural MRI data and show its relevance for disease classification. Based on the magnitude representation of the complex wavelet coefficients calculated from the MR images, we identified a new class of features taking scale, directionality and potentially local information into account simultaneously. By using a linear support vector machine, these features were shown to discriminate significantly between spatially normalized MR images of 41 patients suffering from multiple sclerosis and 26 healthy controls. Interestingly, the decoding accuracies varied strongly among the different scales and it turned out that scales containing low frequency information were partly superior to scales containing high frequency information. Usually, this type of information is neglected since most decoding studies use only the original scale of the data. In conclusion, our proposed method has not only a high potential to assist in the diagnostic process of multiple sclerosis, but can be applied to other diseases or general decoding problems in structural or functional MRI.