Abstract
Abstract
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.
Reference32 articles.
1. Despeckling of medical ultrasound images using data and rate adaptive lossy compression;IEEE Trans Med Imaging,2005
2. A “salt and pepper” noise reduction scheme for digital images based on Support Vector Machines classification and regression;Sci World J,2014
3. A theory for multiresolution signal decomposition: the wavelet representation;IEEE Trans Pattern Anal Mach Intell,1989
4. Noise reduction in MRI liver image using discrete wavelet transform;Int Res J Eng Technol,2016
5. Sparse representation based on vector extension of reduced quaternion matrix for multiscale image denoising;IET Image Process,2016
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献