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     

基于像素融合的Curvelet医学超声图像降噪方法

目的：探求基于Curvelet变换的医学超声图像降噪的有效方法。方法：分别对超声图像进行Curvelet和隐Markov树小波降噪，再采用模糊分区方法对结果图像进行像素融合。结果：实现了基于像素融合的Curvelet医学超声图像降噪。结论：对超声图像的降噪实例表明该方法有效提高了图像的视觉效果，明显抑制了伪影。     

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.     

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.     

基于各向异性扩散和小波变换的磁共振图像降噪和增强方法

降噪是医学图像处理中一个非常重要的问题,传统去噪方法在降低噪声的同时会模糊图像的边缘,各向异性扩散滤波在降低图像噪声的同时能够使图像的边缘得到保持.利用小波变换可以对图像进行多尺度分解,使我们可以在不同尺度上对图像进行处理.本文利用各向异性扩散滤波对MRI图像进行降噪,然后利用平稳小波变换对图像进行增强处理.实验结果表明,该方法在有效去除噪声的同时能够增强图像的细节,有效地提高了图像的质量.     

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.     

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.     

优化平稳小波变换去除功能MRI数据噪声的方法

目的通过优化小波变换多分辨率去噪方法,在保持一定灵敏度的条件下,降低假阳性率。方法在小波重构时较原来的方法保留更多的小波尺度,对原有方法的分析结果进行多人平均,并用模拟数据和视觉实验数据对这些方法进行验证。结果分析模拟数据显示,在α〈0.01条件下,本文方法能在保持一定灵敏度的基础上有效地克服原有方法假阳性率高的缺点。分析实验数据显示,以SPM2为标准,在α〈0.001条件下,本文方法能给出既灵敏又相对准确的结果。结论本文方法能同时兼顾灵敏度和准确度,是原有方法的一种优化。     

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.     

基于整数小波变换和零树编码算法的医学图像压缩研究

现代医疗成象设备产生了大量高价值的医学图像,如何对信息的进行有效的存储、查询以及网络传输是一个亟待解决的问题.本文利用整数小波变换和零树编码算法对医学图像进行了压缩研究,试验表明,同传统JPEG标准相比,相同压缩比下本算法的峰值信噪比有明显的提高,同时本算法具有逐渐显现的特性,能够满足医学图像存储、查询以及网络传输的需求.     

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。     

River network based characterization of errors in remotely sensed rainfall products in hydrological applications

The authors propose a hydrologic evaluation framework for gridded rainfall products. This framework makes use of the Spatial Stream Network (SSN) statistical method to provide spatial characterization of the discrepancies between two gridded rainfall products. The SSN method relies on using stream network length rather than the traditionally used Euclidean distances.It also accounts for the flow connectivity information between the network segments. This concept is relevant in hydrological modeling since rivers transport accumulated precipitation that occurred over different parts of the basins, and stream networks do not represent Euclidean space. To demonstrate, we used this framework to compare the satellite rainfall product called Integrated Multi-satellitE Retrievals for GPM (IMERG) with the ground-based Multi-Radar/Multi-Sensor (MRMS) rainfall product. The results show that the magnitudes of the rainfall discrepancies tend to decrease as rainfall accumulates in the downstream direction. However, the covariance range between these discrepancies is much larger along flow-connected stream network segments than in flow-unconnected stream segments. This in turn could have an effect on the error correlation of the predicted discharges. In addition, the spatial linear models of rainfall errors improved significantly with SSN based models in comparison to pure Euclidean separation distance models.     

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.     

Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation

Due to the influence of sensor malfunction and poor atmospheric condition, missing information is inevitable in optical remotely sensed (RS) data, which limits the availability of RS data. To tackle the inverse problem of missing information recovery, a multiscale adaptive patch reconstruction method was proposed in this letter. Multiscale dictionaries were learned from different sizes of exemplars in the known image region, which were later utilized to infer missing information patch-by-patch via sparse representation. Structure sparsity was incorporated to encourage the filling-in of missing patch on image structures and determine the patch size for further inpainting. Neighboring information was employed to restrain the appearance of the estimated patch, to yield semantically consistent inpainting result. In view of these ideas, we formulate the optimization model of adaptive patch inpainting and reconstruct missing information through a multiscale scheme. Experiments are performed on cloud removal, gaps filling and quantitative product reconstruction, which demonstrate that our method can well preserve spatially continuous structures and consistent textures without artifacts.