中值滤波与各向异性扩散相结合的医学图像滤波方法

医学图像的滤波处理,须保留具有重要诊断意义的边缘细节信息。针对Perona-Malik(PM)各向异性扩散模型遇到强噪声则失效和扩散门限参数K依靠经验选取的不足,提出了一种改进的各向异性扩散算法。将PM算法与中值滤波结合,用经过中值滤波平滑后的梯度模代替原始图像的梯度模,以控制扩散的过程。应用自适应扩散门限(当前邻域内梯度的绝对偏差中值(MAD))和迭代终止准则,提高算法鲁棒性和效率。实验分别对超声心动图、CT图像和Lena图像进行去噪处理,用峰值信噪比(PSNR)和边缘保持能力EPI作为评价标准。实验结果表明,改进算法优于PM算法和Catte-PM方法,在提高信噪比的同时保留了图像的细节信息,可以更好地满足医学图像的使用要求。

Filtering method for medical images based on median filtering and anisotropic diffusion

Medical image filtering process should retain the edge details of diagnostic significance. For Perona-Malik (PM) anisotropic diffusion model experienced failure when dealing with strong noise and choosing parameter K of diffusion threshold relies on experience, this paper proposed an improved anisotropic diffusion algorithm. First, PM was combined with the median filter algorithm, and then the gradient mode of the original image was replaced with the gradient mode from the image which was smoothed by the median filter to control the process of diffusion. While applying the adaptive diffusion threshold (Median Absolute Deviation (MAD) of the gradient in current neighborhood) and iteration termination criteria, the algorithm improved robustness and efficiency of the algorithm. The experiment was operated respectively on echocardiography, CT images and Lena image to denoise, and used Peak Signal-to-Noise Ratio (PSNR) and Edge Preservation Index (EPI) as evaluation criterion. The experimental results show that the improves algorithm outperforms PM algorithm and Catte-PM method for improving PSNR while preserving image detail information, and meets the requirements for application in medical images more effectively.