A wavelet-based method for MRI liver image denoising.

Image denoising stays be a standout amongst the primary issues in the field of image processing. Several image denoising algorithms utilizing wavelet transforms have been presented. This paper deals with the use of wavelet transform for magnetic resonance imaging (MRI) liver image denoising using selected wavelet families and thresholding methods with appropriate decomposition levels. Denoised MRI liver images are compared with the original images to conclude the most suitable parameters (wavelet family, level of decomposition and thresholding type) for the denoising process. The performance of our algorithm is evaluated using the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean square error (MSE). The results show that the Daubechies wavelet family of the tenth order with first and second of the levels of decomposition are the most optimal parameters for MRI liver image denoising.