Contourlet域超声图像自适应降斑算法研究

An adaptive speckle reduction algorithm of ultrasound image in contourlet domain

结合Contourlet系数的结构特点和超声图像相干斑乘性噪声模型,提出了一种新的基于Contourlet变换的斑纹噪声抑制算法。该算法通过计算方差一致性测度(VHM),用局部自适应窗口估计阈值萎缩因子,实现超声图像的降斑处理。实验结果表明,该算法在有效抑制斑纹噪声的同时,更有利于保持图像的边界信息,尤其适用于强噪声背景的超声图像。

Combining the characteristics of the Contourlet coefficients and the multiplicative model of the speckle noise on ultrasound images,a novel speckle reduction algorithm was proposed. By calculating the variance homogeneity Measurement （VHM） ,the locally adaptive window was determined to estimate the shrinkage factor,and then the speckle reduction to ultrasound images was implemented. Experiments show that this algorithm could reduce speckle effectively and retain more boundary information than other methods,it is especially adequate to the images which are contaminated by intense speckle.