Rician噪声水平场的估计及其在MR图像去噪中的应用

针对MR图像中空间变化Rician噪声的抑制问题,提出了一种噪声水平场的估计方法,同时结合方差稳定变换和BM3D算法实现MR图像的去噪.噪声水平场通过Rician噪声水平的局部估计和稀疏性约束模型进行估计,利用噪声水平场对噪声图像幅值进行空间自适应方差稳定变换,使得噪声与信号幅值和空间位置无关,采用BM3D算法即可实现对噪声的抑制,最后通过方差稳定逆变换得到无偏的去噪图像.仿真实验中,噪声水平场估计的平均相对误差小于0.2％,利用空间自适应方差稳定变换进行去噪,相比方差稳定变换,去噪图像的峰值信噪比可提高2 dB;采用真实乳腺MR图像进行去噪实验,利用自适应方差稳定变换可得到较高的Q度量.结果表明,所提出的方法能有效估计Rician噪声水平场,并用于抑制MR图像中空间变化的噪声.     

基于小波软阈值滤波的医学图像去噪

目的:为了更好的去除DR医学图像噪声.方法:通过分析其噪声来源,在小波去噪的基础上进行改进.引入方差不变性变换来调整原始图像的噪声模型为高斯噪声模型.图像分解为不同频率的不同子带的小波系数,分别进行不同阈值的滤波.结果:与普通的全局小波去噪方法相比,该方法不但可以保留图像的边缘信息,而且能提高去噪后图像的峰值信噪比.结论:用此方法处理DR图像在噪声去除、细节质量及骨骼锐化等方面比传统的高斯滤波及小波全局阈值滤波等方法效果要好.     

改进阈值的小波空域相关去噪法在荧光信号处理中的应用

为了去除荧光免疫层析检测中荧光信号的噪声,保留信号的细节信息,提出一种改进阈值的小波空域相关去噪算法。该算法将基于小波变换的空域相关去噪法和软阈值去噪法相结合,根据小波系数相关性的不同和平滑消去阈值法的思想,改进了软阈值去噪法的阈值变量和阈值函数。结果表明,该方法突出了信号边缘,能够有效地去除荧光信号的噪声,去噪后的信号光滑连续,且保留了信号峰的相关细节信息。     

小波变换在心电信号特征提取中的应用

采用分段阈值和模极大值对斜率判据相结合的补偿策略,提出了一种精确提取QRS波群特征值的算法.经过对MIT/BIH心电数据库和临床实测的心电信号的大量实验,结果显示即使在有严重噪声干扰的情况下,运用本算法也很容易实现对QRS波群特征的有效提取,特别是对R波峰具有相当高的定位精度(其误差不超过一个采样点)和分析精度(没有累积误差).     

自适应阈值的小波噪声消除方法及其在消除心电图(ECG)噪声方面的应用

降低或者消除噪声,对得到有用的信号十分重要.例如像ECG这类非平稳信号,其噪声统计特性因为经常受各种因素的影响而变得十分复杂.在本文中,通过将应用小波进行噪声消除和B-Spline(B样条)噪声消除相结合的方法,得到一种新的信号噪声消除技术.试验证明,本文所提出的技术能够抑制噪声,并同时保留信号的细节特征.     

几种阈值分割方法在瞳孔检测中的应用研究

图像分割在瞳孔检测中是一个重要方面,阈值化算法是一种简单、高效的图像分割方法.本文着重研究了三种阈值化方法在瞳孔检测中的应用,通过实验结果对每种方法的特点进行了分析,为在不同情况下的瞳孔检测提供了重要理论依据.     

基于软阈值滤波的小波分析方法在微波致热超声成像系统中的应用

人体组织在微波脉冲的激励下,会因热膨胀产生热超声信号.出于对人体安全的考虑,微波脉冲源的功率不可能太大,因此激励产生的热超声信号的幅度通常很小.在采集这些信号时,如果传感器没有前置放大,则传感器输出的压电信号的信噪比会比较低.本文利用小波域软阈值滤波方法 对未采用前置放大而采样得到的信号进行小波分析,大幅度地提高了信号的信噪比,提取的目标信号位置与理论位置吻合的很好.这表明可以通过小波分析方法 很好地改善热超声信号的信噪比,从而降低对微波致热超声成像采集系统中前置放大器的要求.     

小波变换及其在脑诱发电位分析中的应用

诱发脑是民是研究大脑高级神经过程的一个重要电信号。而从背景噪声中一次提取诱发电位具有非常重要的实用价值。本文介绍了新兴的不上波技术中多分辨率分析方法及其在诱发电位分析和提取中的应用。     

基于小波变换的血管内超声图像血流斑点噪声抑制研究

血管内超声成像已经越来越广泛地应用到冠心病的诊断和介入治疗中.为了提高图像分辨率必须增加超声频率,使得血流斑点噪声也显著增强,降低了管腔和管壁的对比度,增加了识别管壁与周围组织的难度,给病情的诊断和治疗带来了不便.本研究结合小波变换域软阈值滤波法和半软阈值滤波法,并设计了一种局部阈值来实现血流斑点噪声抑制.实验结果表明该方法在抑制斑点噪声的同时保留了图像的边缘,增强了管腔和管壁的哪对比度,有助于识别管壁和周围组织.     

几种图像去噪算法的仿真分析及其在fMRI数据处理中的应用

目的去除图像噪声是医学图像处理过程中的基本预处理步骤,对图像的后继分析处理的质量有重大影响。本文基于图像去噪和医学图像的诊断准确率息息相关这一现实问题,对几种图像去噪算法进行仿真分析,并实现功能磁共振(functional magnetic resonance imaging,f MRI)数据应用。方法首先阐述了几种常用图像去噪算法的基本原理,其次使用不同算法对加入高斯噪声的Lena图像进行去噪仿真,并对不同结果的峰值信噪比(peak signal-to-noise ratio,PSNR)和均方差(mean square error,MSE)进行比较,最后总结并选择最优降噪算法应用于f MRI数据分析中,以期获得较好的后期处理基础。结果小波分层阈值算法在f MRI处理中的峰值信噪比和均方差更优。结论在f MRI图像去噪过程中,利用小波分层阈值算法更能提高图像的信息利用率,有助于提高医师诊断的准确率。     

小波变换在医学图像增强的应用

利用小波变换对MRI医学图像进行增强处理，使原图像中较模糊、对比度差的细节得到增强，其纹理清晰，处理结果优于传统的直方图均衡和Laplace锐化等图像增强方法。     

Texture analysis of foot sole soft tissue images in diabetic neuropathy using wavelet transform

The paper presents a new method of characterisation of texture changes in foot sole soft tissue ultrasound (US) images, as observed to occur in diabetic subjects, using wavelet transforms. US images of the soft tissue subcutaneous layer were taken with a 7.5 MHz linear transducer probe placed parallel to the skin surface. The foot sole hardness was characterised by Shore level. A 2D discrete wavelet transform was performed on the US images to extract features that encode the internal state of the foot sole soft tissue. The global energy feature computed at the output of each wavelet channel was found to achieve excellent delineation between the normal and the diabetic groups. An important finding was a strong correlation, in the order of 0.84 and above, between the feature values that reflect changes in the internal arrangement of the tissue, and the externally measurable hardening of the skin, characterised by the Shore levels, with the latter known to be high for diabetics. A comparison drawn between diabetic ulcer and non-ulcer groups established a change in the order of 122–311% in the textural parameter, as influenced by a corresponding 66.7–200% change in the respective Shore values. Thus US examination of foot sole soft tissue and its texture analysis may serve as sources of valuable information regarding the internal changes taking place with progressive hardening of the soft tissue and thereby help the clinician in taking appropriate preventive measures.     

A comparative study of thin-layer cross-sectional anatomic morphology and CT images of the basal cistern and its application in acute craniocerebral traumas

Aim  To compare the cross-sectional morphologic features of successive thin-layers and CT images of the basal cistern and its application in the diagnosis and management of acute craniocerebral traumas. Materials and methods  Successive thin-layer cross-sectional images of the basal cistern were retrieved from the second Chinese visible human (CVH) data set and observed. A total of 40 healthy volunteers were subjected to 64-slice spiral CT scan of the head, and CT images of the basal cistern were compared with CVH images. A total of 413 patients with acute craniocerebral traumas were subjected to 64-slice spiral CT scan of the head, CT image changes of the basal cistern were observed. Results  Thin-layer cross-sectional images retrieved from the CVH data set clearly displayed the sectional anatomic morphology, morphologic change pattern and important adjacent structures of the basal cistern. The quadrigeminal cistern was pateriform or sellaeform; the suprasellar cistern was hexagonal or pentagonal star-shaped; the ambient cistern encircled both sides of the brainstem like an arc band. CT images of the quadrigeminal and ambient cisterns were similar with their CVH images; however, the morphology of the suprasellar cistern changed substantially. In 413 patients with acute craniocerebral traumas, the basal cistern may be normal, or presented with narrowing, obliteration, shift, hematocele, and pneumatosis. Narrowing or obliteration of the basal cistern occurred mostly at the side of dominant intracranial lesions, and frequently occurred in patients with diffuse axonal injury or combination of SDH + CONT + ICH. Conclusions  Thin-layer cross-sectional images of the basal cistern retrieved from the CVH data set correspond satisfactorily to CT images of the basal cistern. Comparison of the two types of images can provide a sectional anatomic basis for the image identification of acute craniocerebral traumas. A careful observation on the initial CT images of the basal cistern for anatomic morphologic changes will help diagnose acute craniocerebral traumas early, improve the management, and appropriately predict the prognosis of the condition.     

Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images

Background

The analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment.

Methods

The automated method for pelvic CT bone segmentation is a hierarchical approach that combines filtering and histogram equalization, for image enhancement, wavelet analysis and automated seeded region growing. Initial results of segmentation are used to identify the region where bone is present and to target histogram equalization towards the specific area. Speckle Reducing Anisotropic Didffusion (SRAD) filter is applied to accentuate the desired features in the region. Automated seeded region growing is performed to refine the initial bone segmentation results.

Results

The proposed method automatically processes pelvic CT images and produces accurate segmentation. Bone connectivity is achieved and the contours and sizes of bones are true to the actual contour and size displayed in the original image. Results are promising and show great potential for fracture detection and assessing hemorrhage presence and severity.

Conclusion

Preliminary experimental results of the automated method show accurate bone segmentation. The novelty of the method lies in the unique hierarchical combination of image enhancement and segmentation methods that aims at maximizing the advantages of the combined algorithms. The proposed method has the following advantages: it produces accurate bone segmentation with maintaining bone contour and size true to the original image and is suitable for automated bone segmentation from pelvic CT images.

     

子波变换在癫痫脑电信号检测和分析中的应用

介绍了一种新近发展的非线性、多尺度及多分辨信号的分析方法——子波分析在癫痫脑电信号检测和分析中的应用。重点介绍子波分析在脑电信号去噪、自动检测及癫痫发作过程的多尺度特征分析,癫痫发作动力学研究,以及癫痫发作预报等方面的应用。为癫痫脑电信号的临床应用及发病机理的研究提供一种新的方法。     

Segmentation of 3D cell membrane images by PDE methods and its applications

We present a set of techniques that enable us to segment objects from 3D cell membrane images. Particularly, we propose methods for detection of approximate cell nuclei centers, extraction of the inner cell boundaries, the surface of the organism and the intercellular borders—the so called intercellular skeleton. All methods are based on numerical solution of partial differential equations. The center detection problem is represented by a level set equation for advective motion in normal direction with curvature term. In case of the inner cell boundaries and the global surface, we use the generalized subjective surface model. The intercellular borders are segmented by the advective level set equation where the velocity field is given by the gradient of the signed distance function to the segmented inner cell boundaries. The distance function is computed by solving the time relaxed eikonal equation. We describe the mathematical models, explain their numerical approximation and finally we present various possible practical applications on the images of zebrafish embryogenesis—computation of important quantitative characteristics, evaluation of the cell shape, detection of cell divisions and others.     

Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia

Background

Functional Magnetic Resonance Imaging (fMRI) has been proven to be useful for studying brain functions. However, due to the existence of noise and distortion, mapping between the fMRI signal and the actual neural activity is difficult. Because of the difficulty, differential pattern analysis of fMRI brain images for healthy and diseased cases is regarded as an important research topic. From fMRI scans, increased blood ows can be identified as activated brain regions. Also, based on the multi-sliced images of the volume data, fMRI provides the functional information for detecting and analyzing different parts of the brain.

Methods

In this paper, the capability of a hierarchical method that performed an optimization algorithm based on modified maximum model (MCM) in our previous study is evaluated. The optimization algorithm is designed by adopting modified maximum correlation model (MCM) to detect active regions that contain significant responses. Specifically, in the study, the optimization algorithm is examined based on two groups of datasets, dyslexia and healthy subjects to verify the ability of the algorithm that enhances the quality of signal activities in the interested regions of the brain. After verifying the algorithm, discrete wavelet transform (DWT) is applied to identify the difference between healthy and dyslexia subjects.

Results

We successfully showed that our optimization algorithm improves the fMRI signal activity for both healthy and dyslexia subjects. In addition, we found that DWT based features can identify the difference between healthy and dyslexia subjects.

Conclusion

The results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject.

     

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.