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由于超声成像机制使医学超声图像中存在着大量的斑点噪声,这些斑点噪声大大降低了图像的清晰度和质量,给超声诊断带来很大的困难。针对医学超声图像的斑点噪声去噪问题,提出了一种基于帧相关处理、ROF分解和自适应小波阈值的去噪方法,能够在抑制超声图像斑点噪声的同时,尽可能地保留甚至增强图像的细节信息,大大提高图像质量,取得了很好的效果。 相似文献
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本文提出一种改进的Cycle-spinning多帧Contourlet域图像去噪算法。根据视频序列连续帧间存在的运动信息,使用帧间位移矢量来代替平移技术对图像进行Contourlet去噪,基于帧间相关性不同的特点,改进Cycle-spinning变换为加权平均以消除伪吉布斯现象。实验结果显示该方法能有效去除各种类型的图像噪声,保留图像的细节和纹理信息,峰值信噪比(PSNR)有显著提高。 相似文献
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基于小波分析的医学超声图像去噪与增强研究 总被引:3,自引:0,他引:3
去除超声斑点噪声,增强图像以提高图像质量是医学图像处理的重要课题。论文提出基于小波分析的超声图像去噪与增强方法。首先是结合自适应方向加权中值滤波和小波半_软阈值去噪法有效抑制了斑点噪声,保留必要的细节;然后采用基于小波变换高频增强法增强图像并用同态增晰法增强对比度,有效地改善图像的质量;最后从去噪图像和评价指标上与常用斑点去噪法进行了比较。实验表明,该方法优于其他方法,具有实际的应用价值。 相似文献
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超声弹性成像是利用生物组织弹性信息进行成像的影像学检测技术, 但图像上严重的伪影噪声降低了其临床诊断价值。编码激励技术在提高超声回波信号的信噪比、增加探测深度等方面有显著效果, 同时, 超声空间复合方法利用帧间噪声解相关性可有效抑制伪影噪声。为此, 在应用Chirp编码激励技术的基础上, 结合接收端的基于滤波器的弹性成像空间复合去噪算法, 进一步提高弹性图像质量; 利用Field Ⅱ仿真工具, 以Chirp作为激励信号仿真并计算复合的弹性应变。实验结果表明, 该方法能较强抑制弹性图像的伪影噪声, 对比传统弹性成像系统, 弹性图像的信噪比及对比度噪声比有明显的提升。 相似文献
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提出一种新的基于形态重构与自适应小波算法的自动捕获红外弱小目标的方法,处理方法分为帧间与帧内处理两部分。首先论证了形态重构滤波器在完备格理论下可以实现对红外图像背景的重构,其次分析了自适应小波结合了线性小波强抑制噪声特性和形态小波好的保留目标细节特性,在红外图像能够获得更好的去噪性能,最后通过结合自适应阈值处理方法和准则处理实现了帧内处理,利用时间序列上的形态膨胀累加完成帧间处理。通过对三种不同的红外图像序列的仿真处理,实验结果表明自动捕获方法可以快速准确地捕获图像的弱小目标。 相似文献
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Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images 总被引:2,自引:0,他引:2
Ultrasound imaging is widely used for diagnostic purposes among the clinicians. A major problem concerning the ultrasound images is their inherent corruption by the multiplicative speckle noise that hampers the quality of the diagnosis, and reduces the efficiency of the algorithms for automatic image processing. In this paper, we propose a new spatially adaptive wavelet-based method in order to reduce the speckle noise from ultrasound images. A spatially adaptive threshold is introduced for denoising the coefficients of log-transformed ultrasound images. The threshold is obtained from a Bayesian maximum a posteriori estimator that is developed using a symmetric normal inverse Gaussian probability density function (PDF) as a prior for modelling the coefficients of the log-transformed reflectivity. A simple and fast method is provided to estimate the parameters of the prior PDF from the neighbouring coefficients. Extensive simulations are carried out using synthetically speckled and ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio, edge preservation index and structural similarity index and visual quality, and in addition, is able to maintain the diagnostically significant details of ultrasound images. 相似文献
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超声成像是现代医学影像学最重要的诊断技术之一。然而,由于乘性斑点噪声的存在,使得超声成像的发展受到了一定的限制。针对这种问题,提出了一种贝叶斯非局部平均(NLM)滤波算法的改进策略。首先,运用贝叶斯公式推导出适应于超声图像斑点噪声模型的非局部平均滤波器,由此引出了两种图像块之间距离计算的方式——Pearson距离和根距离;其次,为了减轻计算负担,在非局部区域中选取相似图像块时采用图像块预选择的方式来加速算法;另外,根据多次实验,总结出了一种滤波参数和噪声方差的关系,实现了参数的自适应;最后,利用Visual Studio和OpenCV实现了算法,使得程序的运行时间大幅缩短。为了评估所提算法的去噪性能,在幻影图像和真实超声图像上进行了实验,结果表明:与现有的一些经典算法相比,该算法在去除斑点噪声的表现上有很大提升,并且在保留图像边缘和结构细节方面取得了令人满意的结果。 相似文献
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P. S. Hiremath Prema T. Akkasaligar Sharan Badiger 《Pattern Recognition and Image Analysis》2010,20(3):303-315
Ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in
the human body. The main disadvantage of medical ultrasonography is the poor quality of images, which are affected by multiplicative
speckle noise. In this paper, we present a novel method for despeckling medical ultrasound images. The primary goal of speckle
reduction is to remove the speckle without losing much detail contained in an image. To achieve this goal, we make use of
the wavelet transform and apply multi-resolution analysis to localize an image into different frequency components or useful
subbands and then effectively reduce the speckle in the subbands according to the local statistics within the bands. The main
advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless. The objective of
the paper is to investigate the proper selection of wavelet filters and thresholding schemes which yields optimal visual enhancement
of ultrasound images, in particular. We employ the wavelet shrinkage denoising techniques with different wavelet bases and
decomposition levels on the individual subbands to achieve the best acceptable speckle reduction while maintaining the fidelity
of the image and also examine the effects of different thresholding techniques as well as shrinkage rules for denoising ultrasound
images. The proposed method consists of the log transformed original ultrasound image being subjected to wavelet transform,
which is then denoised by a thresholding technique using a shrinkage rule. Experimental results show that the subband decomposition
of ultrasound images, using Bior6.8 and level 3 with soft thresholding based on Bayes shrinkage rule, performs better than
other techniques. The performance is measured in terms of Variance, Mean Square Error (MSE), Signal-to-Noise Ratio (SNR),
Peak SNR (PSNR) and Correlation Coefficient (CC). The results of wavelet shrinkage techniques are compared with common speckle
filters. We observe that the proposed method achieves better visual enhancement of ultrasound images which would lead to more
accurate image analysis by the medical experts. 相似文献
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针对合成孔径雷达(Synthetic Aperture Radar,SAR)图像受到相干斑噪声的干扰,严重影响了SAR图像的后续处理的问题,提出一种在非下采样轮廓变换(Nonsubsampled Contourlet Transform,NSCT)域将中值滤波和邻域收缩法相结合的SAR图像去噪算法。该算法对原始SAR图像进行NSCT分解,得到低频子带和高频子带图像,对低频子带使用中值滤波处理以去除低频子带中的低频噪声,利用NSCT分解系数之间的相关性,使用邻域收缩法对子带图的系数进行收缩,以消除高频子带中的高频噪声。实验证明,该算法与小波域邻域收缩去噪算法和NSCT硬阈值去噪算法相比,在去噪性能和视觉效果方面均有所提高,在消除噪声同时可以较好地保护纹理细节信息。 相似文献
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《Digital Signal Processing》2005,15(5):455-465
This paper presents the comparative study of various wavelet filter based denoising methods according to different thresholding values applied to ultrasound images. An original image is transformed into a multi scale wavelet domain and the wavelet coefficients are processed by a soft thresholding method. The denoised image is the output image obtained from the inverse wavelet transform of the threshold coefficients using Donoho's method. It has been observed that such denoising methods are effective in the sense that they preserve the edge details besides suppressing the noise. The comparative evaluation of the denoising performance is shown using statistical significance tests for different wavelet filters. Image quality parameters such as peak signal-to-noise ratio, normalized mean square error, and correlation coefficient have been used to evaluate the performance of wavelet filters. The performance has also been compared with the adaptive weighted median filtering method. 相似文献
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Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images. 相似文献
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去噪处理是图像处理中较为重要的环节。针对加噪后的图像的直方图进行分析,依据最小错误率贝叶斯决策和均值滤波理论,提出一种基于均值滤波和最小错误率贝叶斯决策的去噪方法。首先对加入噪声后的图像直方图进行统计,从中估计出服从分布的不同类别参数,对图像中每一像素点进行判断是否为噪声,对噪声点进行基于均值滤波的处理。通过试验,取得了良好的效果。 相似文献
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目的 超声图像斑点噪声会影响诊断的准确性和可靠性。通过分析超声图像斑点噪声统计模型,结合非局部均值滤波算法,提出一种基于超声斑点噪声模型的改进权值非局部均值(NLM)滤波算法。方法 算法针对超声图像灰度信息对图像进行预处理,利用超声图像斑点噪声模型改进传统NLM算法的权值计算函数,基于图像特征确定最优采样间隔进行下采样,利用改进后的权值计算函数对图像进行NLM去噪处理。结果 分别采用人工合成与真实超声图像对本文算法性能进行测试,并与传统非局部均值滤波算法、非局部总变分(NLTV)等算法进行去噪效果比较,同时采用均方误差、峰值信噪比和平均结构相似性作为滤波算法性能的客观评价指标。本文算法能快速完成超声图像的去噪处理,峰值信噪比较其他算法可以提高0.2 dB以上,可以降低均方误差,提高平均结构相似性,缩短处理时间,并得到较好的图像质量和视觉效果。结论 根据超声图像斑点噪声模型对NLM算法的权值计算函数进行优化,使得NLM图像滤波算法能更好地适用于超声图像的去噪,基于超声斑点噪声模型的改进权值NLM算法相较于其他算法,滤波效果更佳,适合超声图像去噪。 相似文献
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医学超声图像中固有的斑点噪声严重降低了图像的可解译程度,影响了后续的图像分析和诊断。提出了一种基于冗余小波变换的超声图像去斑算法,首先对含斑图像进行对数变换,将乘性噪声变成加性噪声;再对转换后图像做冗余小波分解;在小波系数服从广义高斯分布的前提下,计算每个小波高频子带的贝叶斯萎缩阈值,利用软阈值方法修正小波系数。实验结果表明,该算法去斑性能优于传统的空间域滤波和正交小波阈值去噪方法。 相似文献