Palm print image recovery and de-noising via multi-channel wavelet filter bank

In this paper, a 3-channel wavelet transform method is introduced to recover palm print images following serious deformation. The deformation processing is actually a kind of digital re-sampling. A filter bank consisting of three filters is implemented for wavelet decomposition of the palm print image, and then a procedure of binary interpolation is performed after the image is reconstructed by another filter bank consisting of another three filters. The design of multi-channel wavelet filter bank is based on the Quadrature Mirror Filter (QMF) method. Because the noise is caused by the Morie stripe, the images are further de-noised after the geometry deformation is addressed. Acceptable results have been obtained.     

基于复小波域非线性扩散的超声图像去噪

超声图像易受斑点噪声的干扰,限制了其在医学诊断中的进一步应用。提出了一种将双树复小波变换(DT-CWT)与非线性扩散相结合的超声图像去噪方法。首先,对图像进行双树复小波分解;然后,高频部分和低频部分分别采用自适应对比度扩散和全变差扩散,最后重构图像。给出了实验结果,并与小波阈值收缩和全变差扩散结合的方法、基于小波和基于多小波的非线性扩散方法的图像去噪效果进行了比较。结果表明,本文提出的方法去噪效果更为优越:不但抑制噪声的能力更强,而且能够更好地保留超声图像原有的边缘和纹理特征。     

MR image reconstruction via guided filter

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.     

Image de-noising by integer wavelet transforms and generalized cross validation

De-noising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and keep or shrink the coefficients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure has linear complexity and is fully automatic, i.e., it does not require an estimate for the noise energy. This paper uses the method for wavelet transforms that map integer gray-scale pixel values to integer wavelet coefficients. An image with artificial noise is used to illustrate the optimality properties of the estimator. Not all theoretical requirements for a successful application of the method are strictly fulfilled in the integer transform case. However, this has little influence on practical results.     

基于小波分析的红外乳腺图像去噪与增强的实验研究

将小波的多分辨率分析技术运用到红外乳腺图像的降噪增强处理以改善图像质量.运用不同的阈值量化策略实现图像降噪.再引入增益因子,采用基于小波变换的增强算法突出肿块阴影.通过实验将直接增强和去噪后再增强的图像进行对比可以得到清晰度更高的图像.经处理后的乳腺图像,为临床提供了更细致明确的信息,有助于提高诊断水平.     

基于低通和非线性滤波的MR图像增强算法

利用图像高频分量的局部特征，构造一种新的非线性滤波剪切阈值，使整幅图像的增强趋于均匀化。结合平滑去噪和非线性滤波图像增强两个步骤，我们提出了一种实用的MR图像增强算法。试验结果表明增强后MR图像主观视觉效果显著改善。     

DR image de-noising and acceleration based on recursive cycle pinning contourlet transformation

目的 数字化X线摄影(digital radiography,DR)图像中的高斯噪声对图像质量影响大,消除此类噪声有利于提高图像质量以辅助医生做出正确的诊断.方法 为抑制DR图像的高斯噪声,首先采用递归循环平移与Contourlet变换结合的(recursive cycle spinning Contourlet transform,RCSCT)方法变换分解DR图像,接着采用连续的二元软阈值函数处理变换系数防止系数被过度扼杀,然后基于CUDA(compute unified device architecture,计算统一设备架构)平台对去噪方法加速.结果 该方法提高了去噪后的图像峰值信噪比,有效抑制了伪吉布斯现象,保留了更多的图像细节信息,并且加速处理后运算耗时较短.结论 本文方法比小波变换和Contourlet变换在保留视觉细节信息方面效果更优,算法耗时少,实用性好.     

基于RCSCT变换的DR图像去噪及加速

目的 数字化X线摄影（digital radiography,DR）图像中的高斯噪声对图像质量影响大,消除此类噪声有利于提高图像质量以辅助医生做出正确的诊断.方法 为抑制DR图像的高斯噪声,首先采用递归循环平移与Contourlet变换结合的（recursive cycle spinning Contourlet transform,RCSCT）方法变换分解DR图像,接着采用连续的二元软阈值函数处理变换系数防止系数被过度扼杀,然后基于CUDA（compute unified device architecture,计算统一设备架构）平台对去噪方法加速.结果 该方法提高了去噪后的图像峰值信噪比,有效抑制了伪吉布斯现象,保留了更多的图像细节信息,并且加速处理后运算耗时较短.结论 本文方法比小波变换和Contourlet变换在保留视觉细节信息方面效果更优,算法耗时少,实用性好.     

Ultrasound grayscale image compression with JPEG and wavelet techniques

The purpose of the study was to evaluate the effects of lossy compression on grayscale ultrasound images to determine how much compression can be applied while still maintaining images that are acceptable for diagnostic purposes. The study considered how the acquisition technique (video frame-grabber versus directly acquired in digital form) influences how much compression can be applied. For directly acquired digital images, the study considered how text (that is burned into the image) affects the compressibility of the image. The lossy compression techniques that were considered include JPEG and a Wavelet algorithm using set partitioning in hierarchical trees (SPIHT).     

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter

In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images.

Open image in new window

Graphical abstract The detailed implementation of the proposed medical image fusion algorithm.

     

基于小波分析的医学图像融合技术及其效果评价

多模医学图像融合技术是近年兴起的一种新技术，在医学影像技术学和放射医学领域中有极为重要的应用价值。本文中我们使用小波分析算法有效的实现了基于像素级的多重图像融合，并给出了量化的评判指标。实验结果表明，基于小波分析的医学图像融合技术能有效的保留原始多源图像的边缘和纹理特征。避免融合图像出现平均化。     

Ultrasound and angio image compression by cosine and wavelet transforms.

The investigation results for improving lossy compression techniques for ultrasound and angio images are presented. The goal was to determine where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that the wavelet transform outperforms the discrete cosine transform applied to ultrasound and angio images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterised by a content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to an algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images. A set of recommendations for acceptable compression ratio for different medical image modalities was developed. It was carried out on the base of compression study performed by the group of angiologists and cardiologists.     

基于同态滤波与直方图均衡化的超声图像增强

背景：同态滤波与直方图均衡化都能单独对图像进行增强处理,但各自还有许多不足之处。 目的：观察将同态滤波与直方图均衡化结合使用对增强图像的效果,减少单独使用这两种方法产生的缺点。 方法：在频域内,首先对超声图像分别进行高通同态滤波和低通同态滤波的分频处理;再将得到的两幅滤波图像线性组合;最后再对该组合图像进行全局的直方图均衡化得到最终的增强超声图像。 结果与结论：实验结果表明,超声图像经过上述方法的处理,边界十分突出明显,且整体视觉效果更明亮清晰。通过分析对比得到的均方根误差和信噪比数据,也证实了实验所用的超声图像增强法是十分切实有效的。     

基于小波变换和一致性检验的多模态医学图像融合算法

目的选择已配准后的多聚焦医学图像以及MRI/CT灰度图像为实验素材,以探究不同的融合策略对图像融合效果的影响。方法在对多模态医学图像融合时,低频融合分别采取了加权平均、取极大值法、区域能量以及区域方差的对比实验。高频融合分别采取了区域能量、区域方差以及滤波后基于邻域窗口的一致性检验的对比实验。结果通过对融合图像主观(融合效果)与客观(灰度直方图、边缘提取、性能评价)对比分析,找到了多模态医学图像融合的最优融合策略。结论当低频选择局部区域方差融合,融合后的图像轮廓清晰、边缘较完整;而高频选择滤波后基于邻域窗口的一致性检验,融合后的图像更好地保留和加强了源图像的细节信息。     

Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data

In this paper, a new image reconstruction scheme is presented based on Tuy's cone-beam inversion scheme and its fan-beam counterpart. It is demonstrated that Tuy's inversion scheme may be used to derive a new framework for fanbeam and cone-beam image reconstruction. In this new framework, images are reconstructed via filtering the backprojection image of differentiated projection data. The new framework is mathematically exact and is applicable to a general source trajectory provided the Tuy data sufficiency condition is satisfied. By choosing a piece-wise constant function for one of the components in the factorized weighting function, the filtering kernel is one dimensional, viz. the filtering process is along a straight line. Thus, the derived image reconstruction algorithm is mathematically exact and efficient. In the cone-beam case, the derived reconstruction algorithm is applicable to a large class of source trajectories where the pi-lines or the generalized pi-lines exist. In addition, the new reconstruction scheme survives the super-short scan mode in both the fan-beam and cone-beam cases provided the data are not transversely truncated. Numerical simulations were conducted to validate the new reconstruction scheme for the fan-beam case.     

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。     

Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.     

Influence of anode and filter material on image quality and glandular dose for screen-film mammography

The influence of anode and filter materials on the performance (image quality and dose) of a mammography system is investigated. The image quality is evaluated with the image quality index method. A computer simulation has been developed to calculate the physical parameters of the image quality index (contrast, resolution and noise) as well as the mean glandular dose. The calculations take into account the successive steps of the process: x-ray production, filtration, interaction with the test object, anti-scatter grid, interaction with the image detector (screen-film system). An excellent correlation is obtained between the results predicted by the model and those of experimental measurements, suggesting that the model may be used for the prediction of the performance of mammographic equipment. The experimental conclusions are confirmed: the use of a tungsten anode with a rhodium filter allows a dose reduction without a significant degradation of image quality. The computer program can also be used to simulate the influence of factors which are difficult to combine in practice, e.g., various anode and filter materials, monoenergetic x-rays, etc.     

Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system

The classification of heart beats is important for automated arrhythmia monitoring devices. The study describes two different classifiers for the identification of premature ventricular complexes (PVCs) in surface ECGs. A decision-tree algorithm based on inductive learning from a training set and a fuzzy rule-based classifier are explained in detail. Traditional features for the classification task are extracted by analysing the heart rate and morphology of the heart beats from a single lead. In addition, a novel set of features based on the use of a filter bank is presented. Filter banks allow for time-frequency-dependent signal processing with low computational effort. The performance of the classifiers is evaluated on the MIT-BIH database following the AAMI recommendations. The decision-tree algorithm has a gross sensitivity of 85.3% and a positive predictivity of 85.2%, whereas the gross sensitivity of the fuzzy rule-bassed system is 81.3%, and the positive predictivity is 80.6%.     

Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system

The classification of heart beats is important for automated arrhythmia monitoring devices. The study describes two different classifiers for the identification of premature ventricular complexes (PVCs) in surface ECGs. A decision-tree algorithm based on inductive learning from a training set and a fuzzy rule-based classifier are explained in detail. Traditional features for the classification task are extracted by analysing the heart rate and morphology of the heart beats from a single lead. In addition, a novel set of features based on the use of a filter bank is presented. Filter banks allow for time-frequency-dependent signal processing with low computational effort. The performance of the classifiers is evaluated on the MIT-BIH database following the AAMI recommendations. The decision-tree algorithm has a gross sensitivity of 85.3% and a positive predictivity of 85.2%, whereas the gross sensitivity of the fuzzy rule-based system is 81.3%, and the positive predictivity is 80.6%.