摘要
超声图像易受斑点噪声的干扰,限制了其在医学诊断中的进一步应用。提出了一种将双树复小波变换(DT-CWT)与非线性扩散相结合的超声图像去噪方法。首先,对图像进行双树复小波分解;然后,高频部分和低频部分分别采用自适应对比度扩散和全变差扩散,最后重构图像。给出了实验结果,并与小波阈值收缩和全变差扩散结合的方法、基于小波和基于多小波的非线性扩散方法的图像去噪效果进行了比较。结果表明,本文提出的方法去噪效果更为优越:不但抑制噪声的能力更强,而且能够更好地保留超声图像原有的边缘和纹理特征。
Ultrasound images are easily corrupted by speckle noise,which limits its further application in medical diagnoses.An image de-noising method combining dual-tree complex wavelet transform（DT-CWT） with nonlinear diffusion is proposed in this paper.Firstly,an image is decomposed by DT-CWT.Then adaptive-contrast-factor diffusion and total variation diffusion are applied to high-frequency component and low-frequency component,respectively.Finally the image is synthesized.The experimental results are given.The comparisons of the image de-noising results are made with those of the image de-noising methods based on the combination of wavelet shrinkage with total variation diffusion,the combination of wavelet/multiwavelet with nonlinear diffusion.It is shown that the proposed image de-noising method based on DT-CWT and nonlinear diffusion can obtain superior results.It can both remove speckle noise and preserve the original edges and textural features more efficiently.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2012年第2期332-336,共5页
Journal of Biomedical Engineering
基金
国家自然科学基金资助课题(60872065)
关键词
超声医学图像
图像去噪
双树复小波变换
全变差扩散
自适应对比度扩散
Medical ultrasound image
Image de-noising
Dual-tree complex wavelet transform（DT-CWT）
Total variation diffusion
Adaptive-contrast-factor diffusion
