A New Method of CT MedicalImages Contrast Enhancement

A new method of contrast enhancement is proposed in the paper using multiscale edge representation of images, and is applied to the field of CT medical image processing. Comparing to the traditional Window technique, our method is adaptive and meets the demand of radiology clinics more better. The clinical experiment results show the practicality and the potential applied value of our methodin the field of CT medical images contrast enhancement.     

The Single Training Sample Extraction of Visual Evoked Potentials Based on Wavelet Transform

Based on the good localization characteristic of the wavelet transform both in time and frequency domain, a de-noising method based on wavelet transform is presented, which can make the extraction of visual evoked potentials in single training sample from the EEG background noise in favor of studying the changes between the single sample response happen. The information is probably related with the different function, appearance and pathologies of the brain. At the same time this method can also be used to remove those signal＇ s artifacts that do not appear with EP within the same scope of time or frequency. The traditional Fourier filter can hardly attain the similar result. This method is different from other wavelet de-noising methods in which different criteria are employed in choosing wavelet coefficient. It has a biggest virtue of noting the differences among the single training sample and making use of the characteristics of high time frequency resolution to reduce the effect of interference factors to a maximum extent within the time scope that EP appear. The experiment result proves that this method is not restricted by the signal-tonoise ratio of evoked potential and electroencephalograph （EEG） and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize the samples which evoked evident response more easily. Therefore, more evident average evoked response could be achieved by de-nosing the signals obtained through averaging out the samples that can evoke evident responses than de-nosing the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single trainings sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.     

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.     

3D Medical Image Interpolation Based on Parametric Cubic Convolution

In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter, which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures： mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.     

The Study of Image Segmentation Based on the Combination of the Wavelet Multi-scale Edge Detection and the Entropy Iterative Threshold Selection

This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection. Image for segmentation is divided into two parts by high- and low-frequency. In the high-frequency part the wavelet multiscale was used for the edge detection, and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method. Through the consideration of the image edge and region, a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs. Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.     

胆固醇细胞化学染色方法的比较与分析

Cholesterol staining is a useful approach for the visualization, localization and quantification of cholesterol in cells or tissues, which is frequently used to investigate the mechanisms of some diseases such as arteriosclerosis, Niemann-Pick disease type C, and Alzheimer's disease. It can be accomplished through various microscopes including light microscope, fluorescent microscope, and electronic microscope. During the past decades, various types of methods for cholesterol staining with different principles have been established for different applications. It is important to choose an appropriate method that is suitable for particular experimental aims, features and conditions. At present, three kinds of methods are frequently applied: filipin fluorescent method, BCθ(a biotinylated and carlsberg protease-nicked derivative of perfringolysin O) toxin method, and cholesterol oxidase-diaminobenzidine(oxidase-DAB) method. Four kinds of methods are scarcely applied: Schultze method, perchloric acid-naphthoquinone method(PAN), digitonin method, and o-phthalaldehyde method. In this review, the principles, advantages, and disadvantages of these methods are compared with the emphasis of the application, sensitivity, and specificity.     

Coherent Point Drift Registration Combined with Image Feature and its Application

A key step of constructing active appearance model is requiring a set of appropriate training shapes with well-defined correspondences. In this paper, we introduce a novel point correspondence method （FB-CPD）, which can improve the accuracy of coherent point drift （CPD） by using the information of image feature. The objective function of the proposed method is defined by both of geometric spatial information and image feature information, and the origin Gaussian mixture model in CPD is modified according to the image feature of points. FB-CPD is tested on the 3D prostate and liver point sets through the simulation experiments. The registration error can be reduced efficiently by FB-CPD. Moreover, the active appearance model constructed by FB-CPD can obtain fine segmentation in 3D CT prostate image. Compared with the original CPD, the overlap ratio of voxels was improved from 88.7% to 90.2% by FB-CPD.     

发射端频率复合弹性成像的仿真研究

Objective No reports has been found to date on whether frequency compounding can improve elastographic image signal to noise ratio (SNRe) and how it affects elastogram performance.In this paper simulations investigation was carried out on transmit-side frequency compounding (TSFC)for elastography.Methods 50 mm×50 mm tissue model was simulated with two round hard inclusions of 10mm diameter uniformly distributed along the tissue central axial line,and their elasticity modulus were 10 times of the background.Then simulation of 3.5 MHz、5 MHz and 7.5 MHz probes were introduced to form compression elastography of the double-lesion model by quasi-static compression method (applied strain 1%).Then,sub-elastograms obtained by the combination of 3.5 MHz and 5 MHz,3.5 MHz and 5 MHz,3.5 MHz and 7.5 MHz were compounded,respectively.Results Before compounding,signal to noise ratio (SNRe) of the various sub-elastograms were 8.42,9.62,10.73,respectively,contrast to noise ratio (CNRe) were 11.35,14.82,18.37,respectively and axial resolutions were 9.83,9.82,9.81.After compounding elastograms,the SNRe were 11.82,13.05,19.45,CNRe were 22.31,27.63,56.12,while axial resolutions were 9.83,9.83,9.83.Conclusion Frequency compounding elastograms have higher SNRe and CNRe than any sub-elastogram before compounding and have no axial resolution loss.The TSFC can improve elastogram performance efficiently and frequency compounding for elastography enhancement is feasible.     

A Study on sEMG Simulation Modeling and Its Decomposition Methods

In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.     

A RVR-based Method for Bias Field Estimation in Brain Magnetic Resonance Images Segmentation

This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging task in brain analysis,while the bias field existed in the images can significantly deteriorate the performance.Most of current parametric bias field correction techniques use a pre-set linear combination of low degree basis functions, the coefficients and the basis function types of which completely determine the field. The proposed RVR method can automatically determine the best combination for the bias field, resulting in a good segmentation in the presence of noise by combining with spatial constrained fuzzy C-means(SCFCM)segmentation. Experiments on simulated T1 images show the efficiency.     

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.     

基于Contourlet阈值法的锥形束CT图像去噪研究

目的：将多尺度分析工具之一的Contourlet变换运用到锥形束CT（CBCT）图像去噪领域,并对Contourlet不同阈值去噪方法进行探讨。提出基于Contourlet变换结合半软阈值方法对锥形束CT去噪,并论证去噪效果。方法：利用Contourlet变换的多尺度多方向性以及平移不变性,对低分辨率锥形束CT图像进行拉普拉斯塔形滤波和方向滤波多层分解后得到变换系数,随后对变换系数采用不同阈值方法进行处理,最后逆序反变换得到去噪后图像。通过软阈值和硬阈值方法在Contourlet变换中的应用,提出半软阈值结合Contourlet变换方法对锥形束CT图像去噪。通过对头,胸,盆腔各10例临床锥形束CT图像的去噪,比较三种阈值去噪效果。结果：半软阈值法在胸部和盆腔部锥形束CT图像去噪中比Contourlet硬阈值去噪在PSNR上平均高出1.40 d B和3.11 d B,但在头部锥形束CT图像处理中无优势,而Contourlet软阈值去噪后的锥形束CT图像在消除噪声的同时,信号自身的能量被消弱最多。结论：本文半软阈值法在一定程度上修正了硬,软阈值函数的缺陷,结合Contourlet变换在处理图像几何结构方面的优势,为锥形束CT图像去噪提供了一个新思路。     

A Novel Algorithm for Microcirculation Image Enhancement

Microcirculation images often have low quality in acquisition process,which affect the following steps of process.This paper introduces enhancement algorithm based on nonsubsampled Contourlet transform (NSCT).It analyzes the characteristics of the microcirculation images generated,and separates microcirculation images to light weight and the reflection weight.It also analyzes the construction method on NSCT and proves that this method can be applied on microcirculation image enhancement algorithm.To correct light weight of microcirculation image and obtain enhancement image the enhancement microcirculation image was not only superior to the original image visually,but also improved objective data obviously.The algorithms provide a new method to microcirculation image pre-processing and guide the latter steps of the image processing.     

A wavelet-based spatially adaptive method for mammographic contrast enhancement

A method aimed at minimizing image noise while optimizing contrast of image features is presented. The method is generic and it is based on local modification of multiscale gradient magnitude values provided by the redundant dyadic wavelet transform. Denoising is accomplished by a spatially adaptive thresholding strategy, taking into account local signal and noise standard deviation. Noise standard deviation is estimated from the background of the mammogram. Contrast enhancement is accomplished by applying a local linear mapping operator on denoised wavelet magnitude values. The operator normalizes local gradient magnitude maxima to the global maximum of the first scale magnitude subimage. Coefficient mapping is controlled by a local gain limit parameter. The processed image is derived by reconstruction from the modified wavelet coefficients. The method is demonstrated with a simulated image with added Gaussian noise, while an initial quantitative performance evaluation using 22 images from the DDSM database was performed. Enhancement was applied globally to each mammogram, using the same local gain limit value. Quantitative contrast and noise metrics were used to evaluate the quality of processed image regions containing verified lesions. Results suggest that the method offers significantly improved performance over conventional and previously reported global wavelet contrast enhancement methods. The average contrast improvement, noise amplification and contrast-to-noise ratio improvement indices were measured as 9.04, 4.86 and 3.04, respectively. In addition, in a pilot preference study, the proposed method demonstrated the highest ranking, among the methods compared. The method was implemented in C++ and integrated into a medical image visualization tool.     

应用连续小波变换检测和增强DSA血管影像的研究

数字血管减影的影像中的血管像具有对比度低的特点。本文用一种全新的思想实现DSA影像中血管像的增强,即先应用小波变换技术检测血管边缘,使其从背景中分离出来,再应用对比度拉伸的方法进一步增强血管同背景的对比度,由于本文使用的连续小波变换可从不同方向上检测影像灰度变化情况,所以检测到的影像边缘更准确全面,而且由于噪声的小波变换的模较小,较好地抑制了减影像中的背景噪音,处理后的血管影像具有三维实体的视觉效果。     

Retinal image restoration by means of blind deconvolution

Retinal imaging plays a key role in the diagnosis and management of ophthalmologic disorders, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Because of the acquisition process, retinal images often suffer from blurring and uneven illumination. This problem may seriously affect disease diagnosis and progression assessment. Here we present a method for color retinal image restoration by means of multichannel blind deconvolution. The method is applied to a pair of retinal images acquired within a lapse of time, ranging from several minutes to months. It consists of a series of preprocessing steps to adjust the images so they comply with the considered degradation model, followed by the estimation of the point-spread function and, ultimately, image deconvolution. The preprocessing is mainly composed of image registration, uneven illumination compensation, and segmentation of areas with structural changes. In addition, we have developed a procedure for the detection and visualization of structural changes. This enables the identification of subtle developments in the retina not caused by variation in illumination or blur. The method was tested on synthetic and real images. Encouraging experimental results show that the method is capable of significant restoration of degraded retinal images.     

超声医学图像滤波和对比度增强新方法

较低的对比度和独有的speckle噪声是影响超声医学图像质量的主要原因,本研究利用各向异性扩散滤波,在去除图像中大量噪声的同时,计算滤波过程中图像信息的丢失,从而得到对比度增强模型中的对比度函数,并利用对比度增强模型达到图像对比度增强的目的。实验结果表明,与滤波后的直方图均衡化后结果相比,不仅能够有效地去除图像中的噪声,也能明显提高图像对比度。因此,本文方法是提高超声医学图像质量的一种有效途径。     

基于Contourlet变换的CT和锥形束CT图像配准算法

目的提出一种基于Contourlet变换,用于放射治疗定位的CT与锥形束CT（cone beam CT,CBCT）图像配准的方法。方法利用Contourlet变换多尺度多方向的分辨特性,将待配准图像进行Contourlet变换分解,分解后的高频方向子带合成梯度图像,采用归一化互信息作为相似性测度,把梯度图像与低频方向子带以加权函数结合,进行临床医学图像的刚性配准,有效弥补了互信息配准中缺少空间信息的不足。结果通过已知空间变换参数图像的配准结果验证了算法的准确性。配准后lO幅图像变换参数的误差极小,且均方根误差接近于0。结论该图像配准算法精确度高,并具有很好的鲁棒性,有助于提高图像引导放射治疗（image guid edradiation therapy,IGRT）中解剖组织结构和靶区的定位精度。     

基于模糊最大熵的医学X增强方射线图像边缘法研究

目的：由于医学X射线图像在数字化成像过程中容易受到成像设备中射线散射、电器噪声以及人体组织结构的复杂性等因素的影响,导致数字医学x射线图像的质量不高。因此,针对数字医学X射线图像对比度较差,目标细节信息不明显的特点,研究了一种基于模糊最大熵的图像边缘增强算法。方法：首先将医学X射线图像从灰阶域变换到模糊域。然后通过最大熵准则确定模糊阈值将医学X射线图像分为目标和背景两部分．并分别对其进行图像增强处理．最后再映射回到灰阶域。结果：本文以主动脉造影X射线图像为例,对其分别进行经典模糊边缘增强、反锐化边缘增强和模糊最大熵边缘增强处理,并对处理后图像的相关参数进行定量分析。结论：结果表明基于模糊最大熵算法处理后的图像质量高．边缘细节信息明显增强,且该算法相比其它两种算法具有更好的抗噪性。     

基于Retinex理论的眼底彩色图像增强算法

眼底彩色图像存在亮度低、对比度差、局部细节丢失等问题,分析已有Retinex图像增强方法存在的问题,在此基础上提出一种改进的基于Retinex理论的眼底彩色图像增强方法。首先提取亮度分量,对亮度通道进行多尺度Retinex增强,改进将图像映射到显示器上的gain/offset 算法以及颜色恢复方法,最后对具有亮度信息的红色通道进行恢复。为验证方法的有效性,以DIARETDB0眼底图像数据库为研究对象,并与多尺度Retinex(MSR)、带色彩恢复的多尺度Retinex(MSRCR)、直方图均衡化(HE)、对比度受限自适应直方图均衡化(CLAHE) 4种经典增强算法进行比较。结果表明,所处理的图像在色彩保护、血管对比度的提升以及图像细节的增强方面比其他图像增强方法有更好的效果,信息熵提高5%～7%,峰值信噪比(PSNR)比传统方法提高1～2倍,客观评价指标明显优于当前常用的眼底图像增强方法,对进一步眼底图像的识别具有重要的意义。