Comparisons of Several New De-Noising Methods for Medical Images

Noise is inevitably introduced to medical images because of various factors in medical imaging. The noise in medical images degrades the quality of images, blurring boundaries and suppressing structural details, thus bring difficulties to medical diagnosis. Therefore, the key to medical image de-noising is to remove the noise while preserving important features. In this paper, we analyze and compare three kinds of representative medical image de-noising algorithms including anisotropic diffusion filtering, bilateral filtering and the sparse representation(SR) based method to provide convenience for targeted choosing of de-noising methods. And the results show: with the noise increasing, the image de-noised by the SR based method always has higher PSNR than that of the other methods, but loses more details. Moreover SR based method takes too long time while anisotropic diffusion filtering takes the shortest time.