基于小波的医学超声图像斑点噪声抑制方法

斑点噪声是超声图像中固有的噪声。本文提出了一种新的去除斑点噪声的方法，这种方法结合中值滤波和多尺度非线性小波软阈值的优点，首先把原网像进行对数转换，然后把对数转换后的图像进行中值滤波处理，从而把转换后的图像分成两部分，对每一部分进行小波分析，假设小波系数服从广义高斯分布（GGD），利用小波系数的统计特性估计出各个部分各个尺度的阈值，最后用软阈值方法对上述两部分分别去噪。实验结果表明，本文提出的方法在有效去除斑点噪声方面，优于中值滤波，维纳滤波和多尺度非线性阈值算法（MSSNT-A）。     

使用二维小波收缩法去除弹性成像蠕虫噪声

本文研究使用二维小波收缩去噪法去除弹性成像过程中产生的蠕虫噪声。先使用Sym8小波函数对含有蠕虫噪声的应变估计值矩阵进行3级二维离散小波分解,并使用Birg-éMassart算法获取二维小波变换的域值;然后分别使用硬域值函数和软域值函数对各尺度的水平方向、垂直方向、对角方向的高频系数进行量化;最后将第3层低频系数和各层被量化后的高频系数进行二维小波重构产生去噪后的弹性图像。仿真结果显示,提出的技术有效去除了弹性成像的蠕虫噪声,增强了弹性图像的信噪比(SNRe)和对比度噪声比(CNRe),提高了弹性图像与理想弹性图的相关系数(е);与二维低通滤波去噪法相比,使用二维小波收缩法产生的弹性图像有更高的SNRe和CNRe,能更清晰地显示硬物边界。同时,仿真结果也表明该技术对不同应变量的弹性图像的蠕虫噪声均能有效抑制。本研究表明二维小波收缩去噪法能有效去除弹性图像的蠕虫噪声并提高弹性图像性能。     

人体肺功能生物电阻抗成像技术

研究的目的在于改进生物电阻抗（EIT）重建图像质量方法。首先,采用自适应多重网格法,依据后验误差的估计,基于自适应网格剖分加速线性方程组的求解,并根据多重网格算法细分相关场域,获得圆形场域的人体呼吸过程图像;然后,研究结合先验知识的图像重建算法,根据肺部组织结构及阻抗特性,采用有限元仿真软件COMSOL求解正问题,获取融合先验知识的灵敏度系数矩阵。人体肺呼吸功能实时成像结果表明,即使采用较少的网格单元,仍可获得较高精度的正问题解,具有较高的图像质量。     

一种基于模糊规则和小波变换的医学图像锐化增强算法

针对医学图像组织间不明显现象，提出了一种基于模糊规则和小波变换的医学图像锐化增强算法（MFRWTE）。为了避免过增强现象和放大噪声，对不同尺度的小波系数进行锐化增强时，首先计算该尺度低频系数中心像素与其邻域像素的相容性，利用模糊规则将像素分为低细节，中细节和高细节三类，然后利用自适应算法计算非线性细节增益系数。最后通过把增益系数与细节小波系数相乘，小波重建后得到增强图像。实验结果表明，提出的算法对图像细节进行增强的同时能够有效地抑制噪声。用户也可以根据图像的特征，方便的通过调节中细节区域增强因子或小波分解层数获得满意的增强效果。     

多尺度MRI断层图像特征提取新技术研究

由于医学图像本身信号噪声大，边缘呈弱信号特征，用传统的图像边缘检测算法提取图像特征常会将图像中的噪声作为边缘提取，不能准确地反映医学图像中有价值的信息(如病灶大小等)。现提出一种改进算法，此法以现有的小波模极大值特征提取算法为基础，利用模糊理论确定隶属函数，提取弱信号边缘并用多尺度融合理论边缘点合成。结果表明，此方法在提取MRI图像特征的同时，可有效地抑制噪声，有助于剔除图像的伪边缘，准确定位图像边缘信息，有利于图像分割重建，便于医生根据图像确定病灶或组织的位置大小等。     

基于K空间的MR截断伪影去除算法

目的根据MR截断伪影产生的原理,设计了一种基于K空间的截断伪影去除算法。方法对伪影图像进行处理后提取K空间高频数据,将高频数据与原伪影图像的K空间融合得出最终完整的K空间,再经过重建达到去除伪影的目的。结果在模拟数据试验和临床图片实验中,MR截断伪影都得到改善。结论本文提出的基于K空间的算法对截断伪影有一定的去除效果,但图像域效果比较微弱,算法有待改进。     

结合各向异性扩散滤波Thin Plate先验正电子发射断层图像重建的算法

背景：在正电子发射断层成像中,MAP重建方法通过引入先验分布约束,可以明显提高重建图像的质量,但不合适的先验分布项可能会造成重建图像过度平滑或出现阶梯状边缘伪影。 目的：针对基于传统局部先验信息的MAP方法易于导致重建图像过平滑或产生阶梯状边缘伪影的问题,提出了一种结合各向异性扩散滤波的、基于Thin Plate先验的改进MAP重建算法。 方法：重建算法由两步组成：基于双向扩散系数的PDE各向异性扩散滤波和基于Thin Plate先验的MAP估计。重建图像通过这两步交替迭代得到。文中采用归一化均方根误差和信噪比定量评价重建图像质量。 结果与结论：结合了基于双向扩散系数的PDE各向异性扩散滤波,并将Thin Plate二次二阶先验模型引入到MAP重建算法中,所获得的重建结果图像在抑制噪声、边缘保持方面取得了良好的效果,SNR、RMSE以及视觉评价等方面均有较大程度的改善。     

基于贝叶斯粗糙集的肺部肿瘤CT图像抗噪算法设计

贝叶斯粗糙集处理噪声数据能力强,分类肺部肿瘤CT图像结果准确,为图像去噪提供精准的图像分类结果。基于此,设计基于贝叶斯粗糙集的肺部肿瘤CT图像抗噪算法,基于贝叶斯粗糙集分类模型进行肺部CT图像分类,约简贝叶斯粗糙集属性和决策规则,基于决策规则预测肺部CT图像类别;对存在肿瘤的CT图像噪声小波系数构建拉普拉斯数学模型,基于贝叶斯最大后验概率估计小波系数概率密度,计算噪声方差和子代小波系数标准差,使去噪算法具备自适应性;基于小波系数的概率密度得到最大后验(maximum a posteriori,MAP)估计值,对该值做小波反变换,实现肺部肿瘤CT图像自适应去噪。结果表明,该算法去除肺部肿瘤CT图像噪声效果好,抗噪能力强,较好保留图像细节特征,视觉效果佳。     

基于小波包变换的医学图像融合方法

为满足医学图像临床辅助诊断和治疗的需要,将小波包变换和自适应算子相结合,提出一种新的医学图像融合算法.算法首先对已配准的医学图像进行小波包分解,并采用自适应算子对小波系数及分解子图像进行处理,通过小波包重建,获得高质量的医学融合图像.该方法克服了小波变换不能兼顾图像高频成分的缺陷,并且可以根据不同的医学图像自动调整融合规则的权重系数,有效避免了设置固定权重系数造成的融合误差.实例融合仿真验证了算法的有效性和先进性.     

基于非广延熵先验的PET图像重建EI北大核心CSCD

最大化后验(MAP)方法已经被广泛应用于解决图像重建的病态问题。先验项的选择一直是研究的热点,但是传统先验形式往往会导致重建图像模糊或者产生阶梯状伪影。本文针对传统先验形式存在的不足,提出了一种基于非广延熵先验的正电子发射成像(PET)迭代重建方法。该方法主要利用最小化非广延熵先验来消除先验信息和估计图像之间的不确定性。我们将此算法在体模图像上进行了测试,并与基于传统先验的MAP方法比较。实验表明,本文算法能更好抑制噪声,获得较好的重建图像质量。     

基于小波变换和盲源分离算法提取事件相关电位

阐述了用小波分解和盲源分离(blind source separation,BSS)算法结合来去除噪声和干扰提取事件相关电位(event-related potential,ERP).采用小波变换分解ERP,抽取出不同频带的细节信息;由小波系数判断选择多个尺度的子带信号,将它们分别与原始ERP组合进行盲分离,方法是极大化信号时间上的可预测性;将分离的结果进一步叠加平均.两类ERP仿真实验结果表明,本文算法提取出的ERP主要成分波明显,易于辨识,信噪比比较单独运用盲分离算法提取出的结果要好.在应用实例中,有效地增强了ERP的μ波.该算法优点在于减少了刺激次数和波形失真,参数变化范围小,在临床上有很好的应用前景.     

Analysis and localization of epileptic events using wavelet packets.

This article compares results obtained in previous studies using time-frequency representations (Wigner-Ville, Choi-Williams and Parametric) and the wavelet transform with those obtained with wavelet packet functions to show new findings about their quality in the analysis of ECoG recordings in human intractable epilepsy: data from 21 patients have been analyzed and processed with four types of wavelet functions, including Orthogonal, Biorthogonal and Non-Orthogonal basis. These functions were compared in order to test their quality to represent spikes in the ECoG. The energy based on the wavelet coefficients to different scales was also calculated. The best results were found with the biorthogonal-6.8 wavelet on 5-7 scales, which gave 0.92 sensitivity, but with a high percentage of false positives; this representation was highly correlated with spike events on time and duration. To improve these results we have studied the wavelet packet coefficients energy. We found that reconstruction wavelet packet coefficients at 4 and 9 nodes contain significant information to characterize the spike event. These nodes' reconstruction coefficients were multiplied and this product was highly correlated with spikes events on time and duration. With this procedure we improved the sensitivity up to 0.96 with the same biorthogonal-6.8 wavelet at four levels. With this technique we do not sacrifice computation time: 896 samples are processed at only 0.16 s, so that it is possible to show the spike scattering path on line, because 896 samples (7 s)/16 channels are processed at 3.13 s.     

小波变换在医学图像融合中的应用

图像融合是医学图像处理中的关键技术。文中探讨了基于小波变换的医学图像融合方法。首先对源图像进行小波多尺度分解,然后采用基于窗口的融合规则进行小波系数融合,最后通过小波逆变换重构融合图像。实验结果表明,该方法能在保留原图像信息的情况下增强融合图像的细节信息。     

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.     

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.     

基于小波变换的多模态医学图像的融合及性能评价

为了研究小波变换分解的尺度和融合策略对图像融合效果的影响。我们选择已配准后的多聚焦医学图像以及MRI/CT灰度图像,在提取图像的低频和高频小波系数时,分别进行单尺度和多尺度分解,融合时采取了基于独立像素点和基于邻域窗口的多种融合策略,深入对比分析各种融合规则对医学图像融合性能的影响。实验结果和性能评价表明:使用局部滤波的操作可以明显改善图像融合的效果,使图像的细节信息更加丰富,而多尺度融合能明显提高融合图像的亮度。     

Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints

Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an effective way for us to enhance the image quality at the matched regions between the prior and current images compared to the existing PICCS algorithm. Compared to the current CBCT imaging protocols, the APICCS algorithm allows an imaging dose reduction of 10-40 times due to the greatly reduced number of projections and lower x-ray tube current level coming from the low-dose protocol.     

An Improved Medical Image Fusion Algorithm for Anatomical and Functional Medical Images

In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data, but, not an appropriate fusion algorithm for anatomical and functional medical images. In this paper, the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively. When choosing high-frequency coefficients, the global gradient of each subimage is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy, so that the fused image can reserve the anatomical image＇ s edge and texture feature. Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.     

小波变换在ECG信号滤波中的应用研究

本文首先介绍了小波变换应用于ECG信号消噪处理中的几种常用滤波方法的原理,分析了它们的滤波性能.然后提出一种小波变换与自适应滤波相结合的心电信号去噪方法,实验证明这种去噪方法可以有效抑制心电信号中的噪声干扰,保持信号的波形特征,是对"运用多分辨率分析方法,去除噪声干扰对应小波分解尺度上细节分量"的滤波方法的一种有效改进,达到较好的滤波效果.     

一种基于模糊均差和小波变换的医学图像去噪方法

小波阈值萎缩法能够有效地去除图像中的噪声,去噪阈值直接影响去噪的效果,而噪声标准差在去噪阈值的确定中起着至关重要的作用。针对医学图像的特点、基于寻找更合适的噪声标准差估计方法,本研究提出了一种新的利用模糊均差代替普通标准方差估计噪声标准差的方法。在各层小波分解的低频图像中利用模糊积分估计噪声标准差,然后确定每一层去噪阈值,进行图像去噪。试验结果表明,本研究算法在去除噪声的同时也较好地保持了图像的细节。