Filtering method for medical images based on median filtering and anisotropic diffusion
FU Li-jua · Journal of Computer Applications · 2014
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.