基于图像中值的Sigmoid函数自适应X线图像增强

目的：改善医学图像质量,使低对比度的图像得到增强。方法：针对数字X线医学图像的特点,提出一种基于图像中值的非线性变换函数的增强处理方法。结果：在变换函数中引入可调节的参数,实现了对X线图像的自适应增强。结论：本文算法将低对比度图像拉伸到一个较大的亮度动态范围,提高了对比度,增强了图像细节信息,是一种有效的对比度增强方法。     

基于精确直方图规格化的医学超声图像增强

目的探求一种改进的精确直方图规格化方法,提高医学超声图像的对比度。方法针对医学超声图像的特点,引入一个有效的分割点,将原始图像分割为背景和前景区域,只对前景区域进行增强。结果本文方法将局部灰度拉伸到一个较大的动态范围,提高了对比度,抑制了医学超声图像背景过增强,保留了图像的细节信息。结论本文在增强图像对比度的同时能够有效地保留图像细节,是一种有效的对比度增强方法。     

基于相似度测量的医学超声图像对比度增强

目的探求一种基于相似度测量的医学超声图像对比度增强方法。方法本文在基于相似度测量的对比度增强方法的基础上,针对医学超声图像的特点,引入了局部模糊分形维数特征,改进了相似度和对比度变换因子的计算方法,同时提出了对比度增强系数的自适应选择方法,通过改变图像局部对比度增强图像。结果大量的实验结果表明处理后的图像对比度得到了明显的提高,同时背景和噪声未出现明显改变,组织细节信息保留完整,图像可读性增强。结论本文算法在增强图像对比度的同时能够有效地保留组织细节信息并抑制噪声过度增强,是一种有效的医学超声图像对比度增强方法。     

基于亮度不变的医学超声图像对比度增强方法

目的探求一种基于亮度不变的医学超声图像对比度增强方法。方法在改进常规直方图均衡化算法的基础上,以最小灰度平均值误差二元直方图均衡化为基础,引入另一个有效分割点。结果实现了一种局部直方图均衡化算法亮度保持三元双向直方图均衡化。结论此算法较其他直方图增强方法更好地抑制了医学超声图像黑色背景过增强,保留了原图像细节信息,保持了原图像亮度,提高了图像对比度。     

基于自适应低通滤波的超声医学图像增强算法

目的 介绍一种超声医学图像增强的有效算法.方法 基于自适应低通滤波器的超声图像增强算法,首先采用对数变换的方法将超声医学图像中存在的乘性噪声变为加性噪声;再通过低通滤波器将对数图像分为高频分量和低频分量,对低频分量采用自适应直方图均衡处理,对高频分量进行加权;然后对低频分量和高频分量进行融合得到增强的对数图像;最后对对数图像进行指数变换得到输出图像.结果 原超声医学图像得到有效增强,边缘细节得以保留.结论 该算法有效地实现了超声医学图像增强,突出了超声图像的细节,改善了视觉效果,并对噪声具有良好的抑制作用.     

基于灰度对比和自适应小波变换的X射线图像增强

背景:X射线检查作为常规的检查方式得到了广泛的应用,然而由于现有技术的局限性,使得X射线图像往往具有灰度对比度低和噪声影响等缺点,因此,现有的X射线图像往往达不到医生的要求.目的:增强和去噪处理对比度较低且含有噪声的X射线图像,以达到易于医生理解和识别的目的.方法:针对空间域处理和变换域处理增强X射线图像的不足,提出了基于灰度对比和自适应小波变换的X射线图像增强算法.首先,选择需要增强和减弱的灰度范围,并根据八邻域灰度对比增强算法对X射线图像进行灰度变换,并用中值滤波算法对图像进行平滑;然后,对X射线图像进行小波分解,并运用相邻分解层之间相关系数的大小来确定细节信号和噪声.结果与结论:应用了灰度对比和自适应小波变换相结合的X射线图像增强算法,把基于空间域增强的方法和基于变换域的方法有机地结合起来,比传统的单一增强方法更为优越.实验结果证明它能自适应地增强X射线图像的灰度对比,使得图像细节的显示更加清晰,同时在一定程度上去除了噪声的干扰,对于灰度对比度较低的图像效果更加明显.     

基于改进SUSAN算法的医学图像边缘检测

背景:医学图像的边缘检测是医学图像处理中的一项重要的技术,也是医学图像进一步处理的基础。目的:运用改进的SUSAN算法对医学图像进行边缘检测,取得更丰富的医学图像边缘信息,以便于医学图像的进一步处理。方法:运用Sobel算子对SUSAN算法进行了改进,采用C++语言编程,并在VC++6.0开发平台上实现了改进算法。结果与结论:实验结果表明,该算法能实现阈值的自适应选取,对医学图像中的低对比度的图像边缘有较好的检测效果。     

基于多尺度形态学的医学图像局部对比度增强

目的:寻找一种更精确有效的医学图像局部对比度增强方法,解决磁共振图像中常出现的局部对比度不足问题。方法:在对已有算法研究的基础上,提出了新算法。新算法的特点是:形态学运算采用了非对偶运算,对比度扩展运算采用加法运算代替乘法运算。为避免灰度偏差,提出了条件归一方法。结果:用实用磁共振图像和模拟实验对新算法进行了测试。新算法在精度、运算速度及对噪声的敏感性方面都优于已有同类算法。结论:利用形态学非对偶运算能够以更精确的方式实现局部对比度增强,灰度级偏差可以通过归一化方法消除。     

基于交互式数据语言MRI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MRI图像质量的作用效果。方法:基于交互式数据语言编程环境,分别采用直方图均衡化、自适应直方图均衡化和直方图规定化对颈髓MRIT2W图像进行增强处理,利用峰值信号噪声比和图像信息熵评价图像暗区细节的表现性能以及图像噪声水平。结果:直方图均衡化对图像暗区细节的增强效果一般,图像整体对比度反而有所下降;自适应直方图均衡化增强细节的同时放大了图像噪声,并在边缘形成伪影;直方图规定化可选择匹配直方图函数的类型,充分显示图像暗区细节,图像噪声水平低于前两种方法。结论:应用直方图增强方法处理颈髓MRIT2加权图像,在图像暗区细节表现和图像低噪声水平上,直方图规定化明显优于直方图均衡化和自适应直方图均衡化。     

基于交互式数据语言MRI图像直方图增强方法的比较冰

目的：比较不同直方图增强方法对改善MRI图像质量的作用效果。 方法：基于交互式数据语言编程环境，分别采用直方图均衡化、自适应直方图均衡化和直方图规定化对颈髓MRIT2W图像进行增强处理，利用峰值信号噪声比和图像信息熵评价图像暗区细节的表现性能以及图像噪声水平。 结果：直方图均衡化对图像暗区细节的增强效果一般，图像整体对比度反而有所下降；自适应直方图均衡化增强细节的同时放大了图像噪声，并在边缘形成伪影；直方图规定化可选择匹配直方图函数的类型，充分显示图像暗区细节，图像噪声水平低于前两种方法。 结论：应用直方图增强方法处理颈髓MRIT2加权图像，在图像暗区细节表现和图像低噪声水平上，直方图规定化明显优于直方图均衡化和自适应直方图均衡化。     

常规SE 序列和高分辨MRI 动态增强诊断胸膜局灶性病变的比较

目的比较常规SE序列与高分辨MR(HR-MR)动态增强扫描显示胸膜局灶性病变的价值。方法对30例胸膜局灶性病变做了常规SE平扫和HR-MR T1WI动态增强扫描;进行了图像对比噪声比(CNR)值的测量,分析比较常规SE序列和动态增强的图像质量、病灶检出情况和病灶与周围胸壁深层结构的显示情况。结果30例胸膜局灶性病变总的CNR值在动态增强早期、增强后期和延迟期均高于T2WI(P均<0.05或<0.01),但具体到不同病种,则不同扫描序列CNR值各不相同。含胶原纤维成分的良性肿瘤T2WI的CNR值均高于动态增强(P均<0.01);胸膜恶性肿瘤动态增强的CNR值则均高于T2WI(P<0.05或<0.01);炎性病变动态增强的CNR值亦高于T2WI(P<0.05或<0.01)。MR动态增强的清晰度、图像质量等评分均高于T2WI(P均<0.01);而T2WI的伪影最多,多于动态增强(P<0.01)。HR-MRI T1WI动态增强扫描可检出所有胸膜局灶性病灶且有助于明确病灶的内部结构及其血流灌注情况,且在显示肿瘤浸润胸壁的深层结构的准确性较常规SE序列要高。结论动态增强HR-MRI扫描可提高胸膜恶性病变和炎症病变的对比噪声比值,能改善图像质量,有助于明确病灶的浸润范围,对胸膜局灶性病变有一定的诊断价值。     

Influence of postprocessing curves on contrast-echographic imaging: preliminary studies.

The aim of this study was to evaluate the influence of postprocessing curves on the visual and quantitative enhancement of image brightness caused by the injection of a contrast agent. A fixed dose (0.45 ml/kg) of contrast agent EchoGen was injected into three dogs, and images of the kidney were investigated using one linear (A) and three nonlinear (B, C, D) postprocessing curves. Curve D gave the best gray scale images and was generally used for diagnostic imaging. Images were analyzed qualitatively in three dogs and quantitatively in one dog for brightness change caused by contrast agent injection. Visually the changes were most identifiable with curve C and least by curve D. Video intensity time curves reflected the qualitative changes. Peak contrast and area under the video density curve changed in the order curve D < curve B approximately curve A < curve C. Choice of the postprocessing curve can have a marked influence on the enhancement of image brightness by a contrast agent. Postprocessing curves optimized to display a wide range of echo levels may not always be an ideal choice for contrast sonography.     

3D multi-scale vessel enhancement filtering based on curvature measurements: application to time-of-flight MRA

In this paper we evaluate the use of voxel intensity curvature measurements to enhance vessels in 3D MRA images. We compare a multi-scale discrete kernel filter (MaxCurve) to the Hessian matrix based filter proposed by Frangi and co-workers. The MaxCurve filter is based on the maximum difference between the negative curvature computed along orthogonal lines defined by a 3x3x3 kernel. Filter performance is assessed using measures of vessel and background separation (contrast and the area under the ROC curve). Filter parameters are optimized using a training set of four typical time-of-flight MRA images and tested on a separate set of ten MRA images with the same acquisition parameters. The filters tended to provide good MIP image contrast enhancement. The filters are applied to MRA images acquired with different parameters and field strengths indicating potential usefulness for a variety of images. Overall the discrete kernel and Hessian matrix filter performed quite similarly.     

Fast OCT image enhancement method based on the sigmoid-energy conservation equation

Optical coherence tomography (OCT) is an important medical diagnosis technology, but OCT images are inevitably interfered by speckle noise and other factors, which greatly reduce the quality of the OCT image. In order to improve the quality of the OCT image quickly, a fast OCT image enhancement method is proposed based on the fusion equation. The proposed method consists of three parts: edge detection, noise suppression, and image fusion. In this paper, the improved wave algorithm is used to detect the image edge and its fine features, and the averaging uncorrelated images method is used to suppress speckle noise and improve image contrast. In order to sharpen image edges while suppressing the speckle noise, a sigmoid-energy conservation equation (SE equation) is designed to fuse the edge detection image and the noise suppression image. The proposed method was tested on two publicly available datasets. Results show that the proposed method can effectively improve image contrast and sharpen image edges while suppressing the speckle noise. Compared with other state-of-the-art methods, the proposed method has better image enhancement effect and speed. Under the same or better enhancement effect, the processing speed of the proposed method is 2 ∼ 34 times faster than other methods.     

PerceptFlow: Real-Time Ultrafast Doppler Image Enhancement Using Deep Convolutional Neural Network and Perceptual Loss

Ultrafast ultrasound is an emerging imaging modality derived from standard medical ultrasound. It allows for a high spatial resolution of 100 μm and a temporal resolution in the millisecond range with techniques such as ultrafast Doppler imaging. Ultrafast Doppler imaging has become a priceless tool for neuroscience, especially for visualizing functional vascular structures and navigating the brain in real time. Yet, the quality of a Doppler image strongly depends on experimental conditions and is easily subject to artifacts and deterioration, especially with transcranial imaging, which often comes at the cost of higher noise and lower sensitivity to small blood vessels. A common solution to better visualize brain vasculature is either accumulating more information, integrating the image over several seconds or using standard filter-based enhancement techniques, which often over-smooth the image, thus failing both to preserve sharp details and to improve our perception of the vasculature. In this study we propose combining the standard Doppler accumulation process with a real-time enhancement strategy, based on deep-learning techniques, using perceptual loss (PerceptFlow). With our perceptual approach, we bypass the need for long integration times to enhance Doppler images. We applied and evaluated our proposed method on transcranial Doppler images of mouse brains, outperforming state-of-the-art filters. We found that, in comparison to standard filters such as the Gaussian filter (GF) and block-matching and 3-D filtering (BM3D), PerceptFlow was capable of reducing background noise with a significant increase in contrast and contrast-to-noise ratio, as well as better preserving details without compromising spatial resolution.     

原发性输尿管癌多层螺旋CT及三维重建诊断价值

目的:探讨多层CT及三维重建技术在原发性输尿管癌中的诊断价值。方法:搜集经手术或活检病理证实的原发性输尿管癌16例,均行多层螺旋CT扫描(MSCT),多平面重建(MPR)、曲面重建(CPR)、最大密度投影(MIP)及容积漫游技术(VRT)处理。结果:原发性输尿管癌轴位CT表现为管腔内软组织肿块伴管壁增厚,增强扫描轻-中度强化,增强峰值位于静脉期,MPR和CPR图像均清晰显示肿瘤侵犯的部位、范围、数目及输尿管壁增厚和周围侵犯情况,CPR图像能在一个平面上显示输尿管全程,MIP和VRT图像类似于静脉尿路造影(IVU)。结论:MSCT及三维重建技术对原发性输尿管癌具有较高的诊断价值,能为临床治疗方案的选择和预后评估提供有力帮助。     

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

Purpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.     

基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。