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。     

同态滤波技术在膜蛋白颗粒图像增强中的应用

目的:为了应用计算机对透射电镜膜蛋白颗粒图像进行颗粒的自动识别与计数处理工作,探索一种改善颗粒图像反差不均匀的校正算法。方法:尝试将同态滤波技术应用于电子束入射-透射模型的膜蛋白颗粒图像处理中。针对颗粒图像明暗反差不均、颗粒形态边界模糊不清、难以辨认的特点,通过实验改造和调整高斯、巴特沃斯、指数高通同态滤波器的参数,并在此基础上重新构造了巴特沃斯带阻同态滤波和指数-巴特沃斯带阻同态滤波方法。通过压缩图像中反映背景的低频信号,展宽表现颗粒细节的高频分量,实现改善图像反差不均的目的。结果:实验所构造的同态滤波器处理颗粒图像的效果各有不同,但均能突出颗粒的细节,减轻背景干扰的影响,实现对图像的非线性校正。结论:经过综合比较5种同态滤波函数对颗粒图像的处理效果,认为指数-巴特沃斯带阻同态滤波方法获得的改善反差不均的效果相对更理想,能更好地满足颗粒的识别与分割的要求。     

基于MATLAB的乳腺钼靶图像直方图增强方法的比较

目的 比较不同直方图增强方法对改善乳腺钼靶图像质量的效果.方法 基于MATLAB编程,分别采用直方图均衡化(HE)、直方图规定化(HS)和对比度受限自适应直方图均衡化(CLAHE)对乳腺钼靶图像进行增强处理;利用峰值信噪比(PSNR)客观评价图像的噪声水平.结果 HE对图像对比度的增强效果一般,图像细节反而有所下降;HS可选择匹配直方图函数的类型,从而选择性增强所需灰度范围,但图像噪声水平在三种方法中最高;GLAHE能很好地增强图像各部分的对比度,且图像噪声水平最低.结论 应用直方图增强方法处理乳腺钼靶图像,在图像噪声水平和灰度增强上,CLAHE明显优亍HE和HS.     

基于直方图均衡化的伽马校正和K-means聚类的舌象苔质分离方法

目的舌苔、舌质分离对后续肿瘤患者舌象的客观化辨证具有重要的意义。常用的算法是基于颜色空间通道舌图像的K-means聚类算法。CIELAB颜色空间的a*通道舌图像相较于其他颜色空间通道的舌图像分割结果稳定,常用于后续的分割。对于部分舌图像,a*通道舌图像的舌苔、舌质虽然具有一定的区分度,但二者的区分度并不是十分明显,影响后续的分割结果。因此,本文提出一种基于直方图均衡化的伽马校正和K-means聚类的舌苔、舌质分离方法。方法采用200幅肿瘤患者的舌图像作为实验样本。首先将舌图像从RGB颜色空间转换到CIELAB颜色空间,对a*通道舌图像进行直方图均衡化增强以及伽马校正,然后利用K-means聚类方法对增强后的舌图像舌苔、舌质分离,得到直方图均衡化以及伽马校正后的a*通道舌图像和分割后的舌苔、舌质图像。为了验证算法的可行性,请5位专业中医医生对200例肿瘤患者的舌图像舌苔、舌质分割效果进行辨析。结果进行直方图均衡化以及伽马校正后的a*通道舌图像舌苔、舌质分割结果明显强于未经处理的a*通道舌图像分割结果。经辨析,分割合格率为97%。结论该方法可以很好地实现舌苔、舌质分离,具有一定的应用价值。     

基于区域生长超声图像分割血管边界反映血管病变

背景：通过分析超声血管图像能反映血管的病变情况。 目的：采用区域生长理论对超声图像进行图像分割,分析边界点的相对位移。 方法：先对视频图像分帧,将动态图像转换为静态图像,采用Gabor滤波、自适应直方图量化去除超声图像噪声,然后运用区域生长法对图像做分割,接着通过开闭运算、sobel算子检测图像边界,最后提取出两条血管边界。 结果与结论：通过Gabor滤波、区域生长法等手段,得到了比较好的分割结果。区域生长法在处理速度上满足了实时性要求,具有一定的通用性。并且通过分析边界点的相对位移曲线,一定程度上反映血管的病变。     

眼底图像增强及评价

针对视网膜眼底图像的特点,提出一种新的增强方法。首先进行形态学高帽变换以弱化背景、校正非均匀光照,然后运用有限对比度自适应直方图均衡化(CLAHE)拉伸对比度,增强灰度动态范围,最后使用二维匹配滤波增强图像中有重要临床诊断意义的细节并抑制噪声。在DIARETDB0数据库中进行实验,并利用一种新的无参考图像评价方法对多种增强效果进行客观比较。结果表明,该方法对正常和病变图像都能有效地弱化背景,增强对比度,对视网膜眼底图像的细节增强效果也同样显著,大大提高了图像质量。     

手持式检耳镜的视频流捕捉与处理

目的：医生借助医学图像观察病症时,医学图像的好坏直接影响医生对患者的诊断结果。外加人类的内耳结构相对身体其他结构较小,对图像的要求更加严格。在现有设备中并没有对输出图像的处理这个功能,从而导致一些疾病的误诊。本文中我们旨在增加图像后处理,提升图像的质量,使得显示出来的视频流数据能够更加清晰。方法：利用Visual Studio2010作为开发平台,建立上位机主窗口。基于第三方函数库Direct Show对视频流进行实时捕捉,并利用Open CV中的直方图均衡算法对视频进行后处理。利用两个程序得到不同的结果 ,一个在传输到上位机之前使用直方图均衡算法。另一个不使用直方图均衡算法,只是单纯将得到的图像输出,显示在PC机上。结果：将使用不同程序得到的两种图像进行对比,其中使用直方图均衡算法的图片,图像效果明显优于未使用算法的图像,特别是在病症处,病症区域的轮廓更加清晰,并且色彩更加柔和。结论：经过对比,我们可以明显看出使用直方图均衡算法的图像效果好,而且利用Open CV和Direct Show进行图像处理算法的编程,方法简单快捷,处理效果好,具有良好的跨平台移植性。     

基于小波变换结合其他预处理方法的周围型早期肺癌CT图像增强研究

探讨增强早期肺癌CT图像的方法.采用小波变换增强图像细节的方法,根据图像特点,同时结合对比度自适应直方图均衡化或(和)自适应滤波去噪法,对10位早期周围型肺癌患者的50张CT图像进行增强.结果表明,处理后图像中肺内结节的边缘锐利、内部密度清晰、周围征象(如胸膜凹陷征等)清楚;各组织器官边缘清晰、层次明显,肺纹理清晰度增加.尤其是低对比度图像,处理后图像质量有较明显提高.基于小波变换结合其他预处理方法,对早期肺癌的CT图像进行增强,可为同类研究提供一定的参考.     

彩色医学内窥镜图像的增强处理方法研究

本文利用一个适应于表达和处理彩色医学内窥镜图像的颜色空间，分别从空间域和频率域两个方面讨论了该类图像的增强处理方法。并结合实例，找出适应直方图均衡化和照明反射模型两种最佳处理方案。     

中值滤波法与梯度法的互补效应在CT图像处理中的应用

单独采用中值滤波法与梯度法的其中一种，不能很好地改善CT图像质量。由于这两种方法具有互补效应，可用中值滤波法与梯度法对图像多次交替处理，使CT图像在去除噪声和保护图像边缘的同时使图像边缘得到增强。     

Comparison of Image Enhancement Methods for the Effective Diagnosis in Successive Whole-Body Bone Scans

Whole-body bone scan is one of the most frequent diagnostic procedures in nuclear medicine. Especially, it plays a significant role in important procedures such as the diagnosis of osseous metastasis and evaluation of osseous tumor response to chemotherapy and radiation therapy. It can also be used to monitor the possibility of any recurrence of the tumor. However, it is a very time-consuming effort for radiologists to quantify subtle interval changes between successive whole-body bone scans because of many variations such as intensity, geometry, and morphology. In this paper, we present the most effective method of image enhancement based on histograms, which may assist radiologists in interpreting successive whole-body bone scans effectively. Forty-eight successive whole-body bone scans from 10 patients were obtained and evaluated using six methods of image enhancement based on histograms: histogram equalization, brightness-preserving bi-histogram equalization, contrast-limited adaptive histogram equalization, end-in search, histogram matching, and exact histogram matching (EHM). Comparison of the results of the different methods was made using three similarity measures peak signal-to-noise ratio, histogram intersection, and structural similarity. Image enhancement of successive bone scans using EHM showed the best results out of the six methods measured for all similarity measures. EHM is the best method of image enhancement based on histograms for diagnosing successive whole-body bone scans. The method for successive whole-body bone scans has the potential to greatly assist radiologists quantify interval changes more accurately and quickly by compensating for the variable nature of intensity information. Consequently, it can improve radiologists’ diagnostic accuracy as well as reduce reading time for detecting interval changes.     

A psychophysical comparison of two methods for adaptive histogram equalization

Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.     

基于自适应邻域的超声图像加权中值滤波

提出了一种自适应邻域中值滤波算法，用于医学超声内窥镜图像的噪声滤除。该方法以图像象素邻域的灰度方差为阈值，进行保持与修复窗口的自适应改变，在有效抑制Speckle噪声的同时，较好保留了图像的细节信息。对本算法与Loupas提出的加权中值滤波算法进行了比较，指出本算法在一定程度上克服了加权中值滤波器的不足并保留了它的优点，对超声内窥镜图像的滤噪有较好的效果。     

Effects of x-ray and CT image enhancements on the robustness and accuracy of a rigid 3D/2D image registration

A rigid body three-dimensional/two-dimensional (3D/2D) registration method has been implemented using mutual information, gradient ascent, and 3D texturemap-based digitally reconstructed radiographs. Nine combinations of commonly used x-ray and computed tomography (CT) image enhancement methods, including window leveling, histogram equalization, and adaptive histogram equalization, were examined to assess their effects on accuracy and robustness of the registration method. From a set of experiments using an anthropomorphic chest phantom, we were able to draw several conclusions. First, the CT and x-ray preprocessing combination with the widest attraction range was the one that linearly stretched the histograms onto the entire display range on both CT and x-ray images. The average attraction ranges of this combination were 71.3 mm and 61.3 deg in the translation and rotation dimensions, respectively, and the average errors were 0.12 deg and 0.47 mm. Second, the combination of the CT image with tissue and bone information and the x-ray images with adaptive histogram equalization also showed subvoxel accuracy, especially the best in the translation dimensions. However, its attraction ranges were the smallest among the examined combinations (on average 36 mm and 19 deg). Last the bone-only information on the CT image did not show convergency property to the correct registration.     

眼底数码图像血管分割预处理关键技术研究

目的本文以眼底图像为研究对象,针对眼底图像血管分割的预处理关键技术进行了深入的研究和探讨。方法首先通过RGB通道选择,显示出较为清晰的血管图像,并通过自适应直方图均衡化增强图像对比度。其次,利用灰度倒置和灰度增强,使细小血管更加清晰,并采用空域滤波对血管轮廓边缘进行增强。最后进行图像填充,去除背景中的噪声干扰。结果通过MATLAB仿真实验,获得了血管脉络清晰的眼底数码图像。结论本研究为血管的自动分割以及血管成分的定性和定量分析奠定了基础。     

一种新的彩色医学图像增强算法

本研究提出了一种新的彩色医学图像增强方法。该方法首先在CY空间中对图像的饱和度进行直方图均衡，再对亮度进行直方图修正，充分考虑了彩色图像中的亮度与饱和度之间的联系及亮度限制了饱和度的范围，从而消除了不可实现颜色。应用该算法对彩色医学眼科图像进行增强处理，实验结果表明本算法具有良好的效果。     

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。     

数字图像增强中灰度变换方法在乳腺X线片中的应用

图像增强技术作为一种基本的图像处理技术,其目的是对原始图像进行加工,得到视觉效果更好更有用的图像.乳腺X线片是当今早期诊断乳腺癌的有效手段,但由于人体肉眼分辨能力有限以及人为疏忽等原因,其中有很大一部分诊断信息没有被人们所利用,因此对乳腺X线片进行增强处理很重要.本文在讨论包括线性变换、非线性变换和直方图均衡化三种用于图像增强灰度变换方法基础上,利用MATLAB编程实现相关算法,将其用于乳腺X线片的处理.结果表明,以上三种灰度变换方法均提高了图像的对比度,改善了图像的显示效果.上述三种灰度变换方法应用于临床,均将有利于医生进行微钙化点检测和疾病诊断.     

Digital contrast enhancement for online portal imaging

Seven digital contrast enhancement algorithms were implemented and evaluated for application to images obtained with an online video portal imaging system. An objective quantitative comparison shows that superior contrast enhancement is obtained using histogram modification techniques. Additional tests made on an image of a humanoid phantom indicate that local (adaptive) histogram modification methods can produce a better contrast in detail than their global counterparts, if the original image is nonuniform in intensity.