一种基于2D-VMD的医学图像去噪算法
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摘要
为了更好地去除掺杂在图像信号中的噪声,提出了一种基于2D-VMD的图像去噪算法。首先对含有噪声信号的图像进行2D-VMD分解。然后利用信噪比与均方误差值将分解后的各IMF分量进行计算筛选,确定噪声项。最后将噪声项去除后,将有效IMF分量进行重构,即完成图像去噪。最后,通过计算去噪前后图像的信噪比和均方误差来验证该方法的有效性。文中将这种算法应用于医学图像处理的信号去噪中,实验结果表明,该算法能很好的将图像中的噪声项去除以达到去噪效果,最大限度上保留了原始信号中的有效成分,并提高图像的信噪比与均方误差值。
A medical image de-noising algorithm based on 2 D-VMD is proposed to remove the noise in the image signal. First of all, the image containing the noise signal is decomposed by 2 D-VMD. Then to calculate the PSNR and the MSE for every IMFs after decomposition, determine which is the most affected by the noise. Finally, reconstructed by the effective IMFs component after the noisy IMF is removed, the image de-noising is completed. In the end, the validity of the method is verified by calculating the PSMR and the MSE of the image before and after de-noising. In this paper, this algorithm is applied to signal processing of medical image de-noising, the experimental results show that the algorithm can be effective to removed the noisy IMF in the image to achieve the higher PSNR and MSE than before, the maximum to retains the effective composition in the original signal and to improve the PSNR and the MSE of the image.
A medical image de-noising algorithm based on 2 D-VMD is proposed to remove the noise in the image signal. First of all, the image containing the noise signal is decomposed by 2 D-VMD. Then to calculate the PSNR and the MSE for every IMFs after decomposition, determine which is the most affected by the noise. Finally, reconstructed by the effective IMFs component after the noisy IMF is removed, the image de-noising is completed. In the end, the validity of the method is verified by calculating the PSMR and the MSE of the image before and after de-noising. In this paper, this algorithm is applied to signal processing of medical image de-noising, the experimental results show that the algorithm can be effective to removed the noisy IMF in the image to achieve the higher PSNR and MSE than before, the maximum to retains the effective composition in the original signal and to improve the PSNR and the MSE of the image.
引文
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[12]张中伟.磁共振图像特征及其对比机制(一):信号、噪声及信噪比[J].影像诊断与介入放射学,2017,26(4):344-347.
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[14]岳相臣.经验模态分解算法应用研究[D].西安:西安电子科技大学,2013.
[15]厉祥,王文波.基于二维经验模态分解的高光谱影像去噪方法[J].激光与红外,2013,43(11):1311-1315.
[16]葛光涛.二维经验模态分解研究及其在图像处理中的应用[D].哈尔滨:哈尔滨工程大学,2009.
[17]姚宏,桑丽萍,李彩云.基于二维经验模态分解与小波变换的农作物图像去噪[J].江苏农业科学,2015,43(4):400-402.
[2]王丽.医学图像配准技术应用研究[D].济南:山东大学,2012.
[3]韦春奇.关于医学图像去噪的方法研究[J].科技视界,2015,(29):155-155.
[4]常璐璐,张化朋.B超医学图像去噪模型的快速算法研究[J].计算机技术与发展,2017,27(3):57-60.
[5]蔡风琴.磁共振图像的去噪问题研究[D].武汉:武汉理工大学,2013.
[6]傅彩霞.小波域图像去噪方法研究及其在磁共振图像中的应用[D].上海:华东师范大学,2006.
[7]滕雅琴,贾文霄,王云玲等.计算机辅助检测系统在CT筛查肺结节中的应用研究[J].中国CT和MRI杂志,2016,14(5):33-35.
[8]DRAGOMIRETSKIY K,ZOSSO D.Variational Mode Decomposition[J].IEEE Transactions on Signal Proce ssing,2014,62(3):531-544.
[9]S.L.HAHN.Hilbert Transforms in Signal Proce ssing[M].Norwood,MA,USA:Artech House,1996.
[10]N.E.HUANG,Z.SHEN,S.R.Long,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceeding of the Royal Society,1998,(454):903-993.
[11]李佩杰,陆镛,白晓清等.基于交替方向乘子法的动态经济调度分散式优化[J].中国电机工程学报,2015,35(10):2428-2435.
[12]张中伟.磁共振图像特征及其对比机制(一):信号、噪声及信噪比[J].影像诊断与介入放射学,2017,26(4):344-347.
[13]袁烨.医学影像学的现状及最新的进展研究[J].中国医药导报,2015,12(28):33-36.
[14]岳相臣.经验模态分解算法应用研究[D].西安:西安电子科技大学,2013.
[15]厉祥,王文波.基于二维经验模态分解的高光谱影像去噪方法[J].激光与红外,2013,43(11):1311-1315.
[16]葛光涛.二维经验模态分解研究及其在图像处理中的应用[D].哈尔滨:哈尔滨工程大学,2009.
[17]姚宏,桑丽萍,李彩云.基于二维经验模态分解与小波变换的农作物图像去噪[J].江苏农业科学,2015,43(4):400-402.