小波包分解域的超声图像非同态滤波降噪算法
Nonhomomorphic filtering for ultrasound images despeckling in wavelet packet domain
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摘要: 针对医学超声图像中固有的斑点噪声,提出了一种基于小波包变换和非同态滤波的超声图像降噪算法。通过对小波包子带系数的邻域相关性进行研究,求解子带系数的归一化自协方差函数进而得到独立于小波包系数的邻域算子,提高了算法对信号和噪声的分离能力。然后对邻域算子和小波包系数进行最小均方误差联合估计得到非同态滤波降噪方程,实现对超声图像中斑点噪声的抑制。和传统医学超声图像降噪算法相比,该算法避免了对噪声的分布作近似处理和同态滤波过程,提高了算法的有效性。仿真和临床超声图像的实验结果征实,该算法不但能更有效的对斑点噪声进行抑制,也更好的保留了图像的细节信息。Abstract: For the inherent speckle noise in medical ultrasound images, this paper presents a denoising algorithm based on wavelet packet transform and nonhomomorphic filtering. In order to improve the capability of the separation of signal and noise, local correlation of wavelet packet decomposition coefficients is analysed in this paper. Then the normalized auto-covariance function of these above coefficients is computed. Thus the independent local observed value of each coefficient is achieved. Last a minimum mean square error estimation, which is combined with the local observed values and wavelet packet coefficients, is applied to get the nonhomomorphic filtering to denoise in wavelet packet domain. The experimental results of synthetic speckle and real ultrasound images show that the proposed algorithm outperforms several common medical ultrasound image denoising methods in terms of speckle reduction and edge preservation indices.