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基于小波与小波包分析的CT图像去噪研究 被引量:7

Study of Medical Computed Tomography Image De-noising Based on Wavelet and Wavelet Packets Analysis
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摘要 目的:小波与小波包分析在医学CT图像噪声抑制方面的应用价值研究。方法:采用MATLAB6.5对512×512的CT图像进行实验。提出了小波局部阈值软硬函数折中消噪方法。并将此方法与小波强制消噪、全局阈值硬函数消噪、全局阈值软函数消噪、及小波包消噪的方法进行了对比。结果:从实验中可以得出小波包消噪效果最好,能够有效的滤除图像中的噪声且边缘效果保持良好,本文提出的小波局部阈值软硬函数折中消噪法也能能够有效的滤除图像中的噪声,效果较小波强制消噪、全局阈值硬函数消噪、全局阈值软函数消噪要好,但是边缘效果及噪声滤除的程度都不及小波包。结论:实验结果表明本文提出的小波局部阈值软硬函数折中消噪方法在小波消噪方面具有一定的价值。 Objective: The study is to probe the practical value of Wavelet and Wavelet Packets analysis in the field of CT image De-noising.Methods: The experiment employed MATLAB 6.5 to analyse 512×512 CT image,the paper proposed a compromise de-noising method of the soft and hard function based on the wavelet partial threshold value.Contrasted with wavelet compulsory de-noising,the hard function de-noising of overall threshold value,the soft function de-noising of overall threshold value,and wavelet packets de-noisi...
作者 薛慧
出处 《中国医学物理学杂志》 CSCD 2011年第2期2541-2545,共5页 Chinese Journal of Medical Physics
关键词 医学CT图像 小波分析 小波包分析 图像去噪 medical Computed Tomography(CT) image wavelet analysis wavelet packets analysis image de-noising
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  • 1Keim H,Trcker D,Mallats G,etal.On denoising and bestsignal representation[J].IEEE Trans action Information Theory,1999,5(7):2225-2238.
  • 2Jansen M,Malfait M,Bultheel A.Generalized cross validation for wavelet thresholding[J].Signal Processing,1997,56(1):33-44.
  • 3[9]You Yuli, Kaveh D. Fourth-order partial differential equations for noise removal[J]. IEEE Trans. Image Processing, 2000,9(10):1723~1730.
  • 4[10]Bouman C, Sauer K. A generalized Gaussian image model of edge preserving map estimation[J]. IEEE Trans. Image Processing, 1993,2(3):296~310.
  • 5[11]Ching P C, So H C, Wu S Q. On wavelet denoising and its applications to time delay estimation[J]. IEEE Trans. Signal Processing,1999,47(10):2879~2882.
  • 6[12]Deng Liping, Harris J G. Wavelet denoising of chirp-like signals in the Fourier domain[A]. In:Proceedings of the IEEE International Symposium on Circuits and Systems[C]. Orlando USA, 1999:Ⅲ-540-Ⅲ-543.
  • 7[13]Gunawan D. Denoising images using wavelet transform[A]. In:Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing[C]. Victoria BC,USA, 1999:83~85.
  • 8[14]Baraniuk R G. Wavelet soft-thresholding of time-frequency representations[A]. In:Proceedings of IEEE International Conference on Image Processing[C]. Texas USA,1994:71~74.
  • 9[15]Lun D P K, Hsung T C. Image denoising using wavelet transform modulus sum[A]. In:Proceedings of the 4th International Conference on Signal Processing[C]. Beijing China,1998:1113~1116.
  • 10[16]Hsung T C, Chan T C L, Lun D P K et al. Embedded singularity detection zerotree wavelet coding[A].In:Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan, 1999:274~278.

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