北京生物医学工程

基于变分法的超声图像斑点噪声自适应滤波算法

An Adaptive Benoising Algorithm for Ultrasound Image Based on Variations

作者：张琼沈民奋方若宇

单位：中国科学技术大学电子科学技术系 (安徽合肥230026)

分类号：

出版年·卷·期（页码）：2011·30·3（253-258）

摘要：

各向异性扩散模型在去除超声图像斑点噪声时不能有效保护图像细节,针对上述问题本文提出基于变分法的自适应最小能量去噪模型。首先直接将由微分方程表示的各向异性扩散模型转化为最小能量变分模型；然后引入欧拉弹性能量模型,在去除噪声的同时有效地保护和增强图像细节。同时为了解决数值求解过程中出现的迭代次数与迭代步长的矛盾，本文还提出迭代停止准则和自适应变步长去噪算法。仿真和真实超声图像的实验结果表明基于变分法的超声图像斑点噪声自适应滤波算法在去噪的同时能够很好地保护细节信息，而且能有效地减少迭代次数。

In order to solve the problem that speckle reducing anisotropic diffusion model can not effectively preserve ultrasound image edges while filtering, an adaptive smallest energy denoising algorithm based on variations is proposed. First, a minimum-energy variation model is derived from anisotropic diffusion differential equation. And the Euler’s elastic energy model is introduced to enhance the ability of edge-preservation while filtering. Then in order to solve the conflict between iteration times and step sizes, an iteration-stopping criterion and an adaptive step-size iteration speckle removal scheme are proposed. To evaluate the proposed algorithm, both synthetically introduced speckle images and real medical ultrasound are used. The simulation results show that the proposed method not only preserves image details effectively while filtering, but also reduces the iteration times.

参考文献：

［1］Vogt M, Ermert H. Limited-Angle spatial compound imaging of skin with high-frequency ultrasound ［C］. Ultrasounics Symposium, IEEE, IUS, 2007:240-243.
［2］Lavarello RJ, Sanchez JR, Oelze ML. Improving the quality of QUS imaging using full angular spatial compounding ［C］. IUS IEEE, 2008:32-35.
［3］Frost VS, Stiles JA, Shanmugan KS, et al. A model for radar images and its application to adaptive digital filtering of multiplicative noise ［J］. IEEE Trans Pattern Anal, 1982, 4(2):157-165.
［4］Lee JS. Improved Sigma Filter for Speckle Filtering of SAR Imagery ［J］. IEEE Tran On Geoscience and Remote Sensing, 2009, 47 (1): 202-213.
［5］Chang SG,Yu B,Vetterli M. Adaptive wavelet thresholding for image denoising and compression ［J］. IEEE Trans Med Imag, 2001, 20(8):772-783.
［6］闫肃,赵久奋,王顺宏. 一种改进的相干斑点噪声抑制算法［J］. 计算机仿真,2009,26(3):8-11.
［7］Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion ［J］. IEEE Trans Pattern Anal Mach Intell, 1990, 12(7): 629-639.
［8］Yu YJ, Actons T. Speckle reducing anisotropic diffusion ［J］. IEEE Trans Image Proccess, 2002, 11(11): 1260-1270.
［9］Univ SKK,Sci S,Phys D, et al. Variational discretization of Euler’s Elastica problem ［J］. Journal of the Physical Society of Japan, 2006, 75 (6): 64007-1-64007-4.
［10］Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: From error visibility to structural similarity ［J］. IEEE Trans Image Process, 2004,13(4):600-612.
［11］才辉,张光新，张浩，等. 一种新的机遇多信息测度融合的边缘检测方法［J］.浙江大学学报:工学版，2008，42(10): 1671-1675.
［12］Jensen JA. Field: A program for simulating ultrasound systems ［C］. Med Biol Eng Comp, 10th Nordic-Baltic Conference on Biomedical Imaging, 1996b, 4, Supplement 1, Part 1:351-353.

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