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1.
降噪是医学图像处理中一个非常重要的问题,传统去噪方法在降低噪声的同时会模糊图像的边缘,各向异性扩散滤波在降低图像噪声的同时能够使图像的边缘得到保持.利用小波变换可以对图像进行多尺度分解,使我们可以在不同尺度上对图像进行处理.本文利用各向异性扩散滤波对MRI图像进行降噪,然后利用平稳小波变换对图像进行增强处理.实验结果表明,该方法在有效去除噪声的同时能够增强图像的细节,有效地提高了图像的质量.  相似文献   

2.
目的 探求一种有效的超声医学图像去噪方法.方法 在分析维纳滤波和基于自适应前处理的多尺度小波非线性阈值斑点噪声抑制方法(MSSNT-A)的基础上,提出一种基于维纳滤波与MSSNT-A相融合的超声医学图像去噪方法.利用该方法首先对加噪图像分别进行维纳滤波和MSSNT-A去噪.然后提取维纳滤波处理后的图像边缘,再将其与MSSNT-A去噪后的图像的_柑应像素点进行融合,得到去噪图像.结果 有效地去除了斑点噪声,图像的细节得到保留.结论 与维纳滤波和MSSNT-A方法相比,该方法在有效去除斑点噪声的同时,很好地保留了图像边缘和图像细节信息.  相似文献   

3.
背景:由于人体解剖结构的复杂性、组织器官形状的不规则性及不同个体间的差异性,所以比较适合用多重分形来分析.目的:采用多重分形理论对医学图像进行图像分割.方法:采用基于容量测度的多重分形谱计算及基于概率测度的多重分形谱计算方法对图像进行分割.对于待处理图片分别进行传统的区域生长分割,max容量测度图像分割,sum容量测度图像分割,概率测度图像分割等4种分割,并加入噪声后再进行同样的分割处理作为比较.结果与结论:采用的两种基于多重分形谱的计算法中,基于容量测量的多重分形谱计算方法的关键是定义合适的测度μα;基于概率测度的多重分形谱计算方法的关键是定义合适的归一化概率Pi,不同的测度(概率)和不同的阈值对结果的影像比较大.基于概率测度的方法对噪声比较敏感,但是在滤过噪声时对图像象素大小变化比较大、比较复杂的图像有较好的分割效果.实验表明基于多重分形谱的医学图像分割方法在选择合适的测度(概率)和阈值时是可行的,特别是在较为复杂的图像处理中对于纹理和边缘的区别上有较大的优势,在准确地分割的同时能保留更多的细节,具有重要的实际意义.同时,多重分形也可以作为一种图像的特征,为特征提取多提供一种有力的数据.  相似文献   

4.
背景:在人脑MRI图像中感兴趣区域提取中,应用数学形态学方法取得了较好的效果,但是在抗噪性能和结构元素选取时存在一些不足之处,使得提取效果有缺陷.目的:在数学形态学的基础上,采用一系列改进的数学形态学方法,以期清晰完整地提取人脑MR图像中的感兴趣区域如脑脊液部位,为医学诊断提供准确信息.方法:首先采用复合形态学滤波去除脉冲和高斯噪声,用高低帽变换进行图像增强,然后用形态分水岭阈值分割提取脑部各成分,对分割出的脑脊液图像进行形态开闭滤波、边缘跟踪和灰度填充后,运用抗噪型边缘检测算子检测出清晰完整的脑脊液区域边缘,最后在原图像中用彩色标定,突出感兴趣区域.结果与结论:综合应用多种数学形态学算法,清晰完整地提取了人脑MRI图像中的感兴趣区域--脑脊液部位.经验证,该方法具有简单、快速、精度高、适用性强等特点.  相似文献   

5.
背景:眼底荧光血管造影可反映视网膜血管结构、血流动力学改变、血管病理生理变化及其相关结构的病理改变,广泛应用于视网膜、脉络膜及视神经疾病的鉴别诊断.目的:通过分析视网膜血管的特点和小波边缘检测算法的优缺点,对小波边缘检测算法进行改进,并利用该算法对视网膜血管进行边缘检测与宽度测量.方法:首先由样条函数构造样条小波,由此得到小波滤波系数.根据改进的小波边缘检测方法得到眼底荧光图像的血管初始边缘,利用边缘细化算法对初始边缘进行细化,基于分形技术对细化后的边缘进行连接,由噪声去除算法消除边缘图像中的噪声点,得到连续的单象素点血管边缘.通过两条边缘之间垂直线上的象素点数,得到血管的实际内径.结果与结论:文章使用的方法较好的解决了传统方法边缘细节和噪声太多的问题,具有更好的边缘连续性和更少的过检测点.将其应用于视网膜血管宽度的测量之中,诊断结果与实际情况较为接近,可以为眼科医生的临床诊断提供较大帮助.  相似文献   

6.
目的 为了分析3D均值中值滤波参数的选取对OSEM重建图像的影响,采用3D均值中值滤波器对重建前的投影数据进行滤波处理,并调整参数进行定量分析。资料与方法 对分析模拟软件ASIM模拟的三维模体投影数据进行3D 均值中值滤波处理。利用开源断层图像重建软件STIR中的OSEM算法对滤波前后的投影数据进行重建,视觉和定量评估重建图像。结果 滤波参数与图像质量密切相关。值过大,重建图像边缘保持效果较差,图像过平滑。值过小,不能抑制噪声,导致重建图像细节模糊。 结论 重建图像的噪声大小以及边缘保持效果对滤波参数的选取是十分敏感的。可根据梯度分布直方图确定滤波参数选取范围,再结合梯度分布比从该范围内折中选择合适的参数,达到既去除噪声又保护图像边缘的目的。  相似文献   

7.
基于小波变换的医学超声图像去噪及增强方法   总被引:9,自引:3,他引:6       下载免费PDF全文
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。  相似文献   

8.
基于自适应低通滤波的超声医学图像增强算法   总被引:2,自引:1,他引:1       下载免费PDF全文
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.  相似文献   

9.
超声图像预处理方法的改进研究   总被引:1,自引:0,他引:1  
目的在对超声图像传统预处理方法进行处理后效果对比分析的基础上,提出一些改进措施。方法以超声医学影像为关键点,分析其噪声,提取噪声特征,从空间域处理技术来阐述超声医学影像的预处理方法。结果生物医学图像预处理技术是医学图像系统中最关键的技术之一,直接影响医生的正确诊断,以及后期的医学数据分析。对原始图像经过双精度处理,图像整体亮度会被拉低。变换图像窗口,选择感兴趣的灰度区域进行灰度提升,增强图像的灰度可视信息,对比未做双精度处理前的实验。结论采用新的灰度处理方法后的图像,具有平滑的边缘,清晰的细节,能更好地保留细节信息,具有一定的现实意义和实用价值。  相似文献   

10.
目的:探讨基于多分辨率分析理论的小波域Wiener滤波方法,并观察其在功能磁共振数据分析中的降噪效果。方法:①小波域Wiener滤波方法介绍:时间域高分辨率功能磁共振图像常常带有大量噪声,并且其分布是不确定的,基于多分辨率分析理论的小波域Wiener滤波将不确定分布的一般噪声转换到小波域相互正交的不同尺度空间上,使其在同一尺度空间上为互不相关的附加高斯分布。应用小波削弱算法Wiener滤波分别在不同尺度空间上进行降噪,同时对同一尺度空间信号使用了稳健中位数法进行估计。②应用该方法分别对模拟数据(一方波加上随机噪声,主要是模拟实际的功能磁共振方波)和真实数据进行分析及处理。结果:①模拟数据去噪前、后信噪比分别为68.987,78.529,表明降噪后信噪比明显提高;加噪声的混合数据、经小波处理后的数据与未加噪声的原数据很接近,附加的噪声得到了明显的抑制。②真实数据计算结果表明,经小波域Wiener滤波降噪以后,提高了数据的信噪比,从而能够更有效地提取信号,有助于识别新的有效激活区或排除伪激活区。结论:在功能磁共振数据分析中应用小波域Wiener滤波方法降噪是有效的。  相似文献   

11.
Purpose The edge response behavior of multi-detector row computed tomography (MDCT) in high-spatial- frequency sampling may diminish due to fluctuations, so a method for improving the edge response was developed and tested. Method MDCT enables thin-slice and high-speed scanning compared with conventional single-detector row CT (SDCT). However, MDCT uses increased volume scanning with a simultaneous increase in the radiation dose to patients. Recently, we proposed a fluctuation reduction method using high-spatial-frequency data sampling; however, the edge response in the processed image decreased. In this research, we investigate the edge response behavior in the high-spatial-frequency sampling, and propose a method for improving the edge response. To verify this method, a large water phantom that consists of five resinous rods and a small phantom with a similitude rate of 0.5, which is topologically similar to the former large phantom were scanned, and projection data sampling using high-spatial-frequency was simulated. Thereafter, reconstructed images were obtained by averaging the high-spatial-frequency sampling data, edge gradients of profiles were calculated, and the increased rate of the gradient values were evaluated. Results This method increased the image noise slightly and provided higher gradient values with the same image matrix size as the conventional scans could be obtained without special image processing. In this phantom study, in order to simulate the high-spatial-frequency sampling, a large phantom was scanned and the fluctuation of transmitted X-rays was increased, thereby increasing the noise. Conclusion A phantom study of projection data sampling by high-spatial-frequency sampling was simulated in the x- and y-direction by scanning two phantoms, and the improvement in the edge response by this method produced 25–97% improvement using double-spatial-frequency sampling. If low-noise or high-sensitivity detector is developed, this method may be more effective.  相似文献   

12.
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.  相似文献   

13.
目的 利用小波变换进行医学图像去噪.方法 通过分析二进小波变换下小波极大模值的特点,即信号的极大模值往往会大于噪声的极大模值,而且噪声的极大模值会随着尺度增大而急剧减少,信号的极大模值却改变很小,由此构造了更有效的去噪准则,即根据不同尺度上的极大模值信息,选择不同的域值来滤除噪声.结果 应用该方法进行医学图像去噪,能保持较高的峰值信噪比、图像细节和边缘特征以及图像清晰度.结论 基于小波极大模值信息的去噪方法能有效地降低医学图像中的噪声.  相似文献   

14.
Optical coherence tomography (OCT) is a high-resolution non-invasive 3D imaging modality, which has been widely used for biomedical research and clinical studies. The presence of noise on OCT images is inevitable which will cause problems for post-image processing and diagnosis. The frame-averaging technique that acquires multiple OCT images at the same or adjacent locations can enhance the image quality significantly. Both conventional frame averaging methods and deep learning-based methods using averaged frames as ground truth have been reported. However, conventional averaging methods suffer from the limitation of long image acquisition time, while deep learning-based methods require complicated and tedious ground truth label preparation. In this work, we report a deep learning-based noise reduction method that does not require clean images as ground truth for model training. Three network structures, including Unet, super-resolution residual network (SRResNet), and our modified asymmetric convolution-SRResNet (AC-SRResNet), were trained and evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation index (EPI) and computation time (CT). The effectiveness of these three trained models on OCT images of different samples and different systems was also investigated and confirmed. The SNR improvement for different sample images for L2-loss-trained Unet, SRResNet, and AC-SRResNet are 20.83 dB, 24.88 dB, and 22.19 dB, respectively. The SNR improvement for public images from different system for L1-loss-trained Unet, SRResNet, and AC-SRResNet are 19.36 dB, 20.11 dB, and 22.15 dB, respectively. AC-SRResNet and SRResNet demonstrate better denoising effect than Unet with longer computation time. AC-SRResNet demonstrates better edge preservation capability than SRResNet while Unet is close to AC-SRResNet. Eventually, we incorporated Unet, SRResNet, and AC-SRResNet into our graphic processing unit accelerated OCT imaging system for online noise reduction evaluation. Real-time noise reduction for OCT images with size of 512×512 pixels for Unet, SRResNet, and AC-SRResNet at 64 fps, 19 fps, and 17 fps were achieved respectively.  相似文献   

15.
For a poor quality optical coherence tomography (OCT) image, quality enhancement is limited to speckle residue and edge blur as well as texture loss, especially at the background region near edges. To solve this problem, in this paper we propose a de-speckling method based on the convolutional neural network (CNN). In the proposed method, we use a deep nonlinear CNN mapping model in the serial architecture, here named as OCTNet. Our OCTNet in the proposed method can fully utilize the deep information on speckles and edges as well as fine textures of an original OCT image. And also we construct an available pertinent dataset by combining three existing methods to train the model. With the proposed method, we can accurately get the speckle noise from an original OCT image. We test our method on four experimental human retinal OCT images and also compare it with three state-of-the-art methods, including the adaptive complex diffusion (ACD) method and the curvelet shrinkage (Curvelet) method as well as the shearlet-based total variation (STV) method. The performance of these methods is quantitatively evaluated in terms of image distinguishability, contrast, smoothness and edge sharpness, and also qualitatively analyzed at aspects of speckle reduction, texture protection and edge preservation. The experimental results show that our OCTNet can reduce the speckle noise and protect the structural information as well as preserve the edge features effectively and simultaneously, even where the background region near edges. And also our OCTNet has full advantages on excellent generalization, adaptiveness, robust and batch performance. These advantages make our method be suitable to process a great mass of different images rapidly without any parameter fine-turning under a time-constrained real-time situation.  相似文献   

16.
The empirical mode decomposition (EMD) has been widely applied in filtering synthetic aperture radar interferograms. A noisy interferogram can be adaptively decomposed into different frequency modes by EMD. Then, the noise can be eliminated based on the partial reconstruction of relevant modes. However, most fine detail and noise of an interferogram often locate in the same mode, which will lead to an inaccurate estimation of noise level in a local region. In this paper, we proposed an improved filtering method based on bivariate EMD. The idea of our method is to decompose both the phase image and pseudo-coherence map of an interferogram using EMD. The filter level of an interferogram is then controlled by the parameters calculated from the bivariate EMD components. The quantitative results from both simulated and real data show that the bivariate EMD filtering method outperforms the original univariate EMD-based methods. It could achieve a balance between suppressing noise and preserving fine detail of an interferogram.  相似文献   

17.
Intensity based registration (e.g., mutual information) suffers from a scalloping artifact giving rise to local maxima and sometimes a biased global maximum in a similarity objective function. Here, we demonstrate that scalloping is principally due to the noise reduction filtering that occurs when image samples are interpolated. Typically at a much smaller scale (100 times less in our test cases), there are also fluctuations in the similarity objective function due to interpolation of the signal and to sampling of a continuous, band-limited image signal. Focusing on the larger problem from noise, we show that this phenomenon can even bias global maxima, giving inaccurate registrations. This phenomenon is readily seen when one registers an image onto itself with different noise realizations but is absent when the same noise realization is present in both images. For linear interpolation, local maxima and global bias are removed if one filters the interpolated image using a new constant variance filter for linear interpolation (cv-lin filter), which equalizes the variance across the interpolated image. We use 2D synthetic and MR images and characterize the effect of cv-lin on similarity objective functions. With a reduction of local and biased maxima, image registration becomes more robust and accurate. An efficient implementation adds insignificant computation time per iteration, and because optimization proceeds more smoothly, sometimes fewer iterations are needed.  相似文献   

18.
In this paper, we propose a new post formation adaptive image filtering technique, to be called the homogeneous region growing mean filter, in order to reduce speckle noise with edge preservation in medical ultrasonic images. First, to find a proper seed region, an initially assumed seed region, which is larger than the average speckle size around a given filtering point, is successively contracted according to a certain local homogeneity criterion. Once the seed region is determined, the next step is to grow the homogeneous region successively based on some measures of local homogeneity and similarity of the neighboring region. The output of the proposed filter for each filtering point is obtained from the arithmetic mean of the grown locally homogeneous region. Several simulation results are presented to illustrate the performance of the proposed filter. They show that the proposed technique effectively smoothes ultrasonic speckle and completely suppresses isolated impulsive noise over the entire texture in addition to preserving the edge information.  相似文献   

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