Research Article Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts Yanqiu Zeng , 1 Baocan Zhang, 1 Wei Zhao , 1 Shixiao Xiao, 1 Guokai Zhang , 2 Haiping Ren , 3 Wenbing Zhao, 4 Yonghong Peng , 5 Yutian Xiao, 6 Yiwen Lu, 7 Yongshuo Zong, 7 and Yimin Ding 8 1 Chengyi University College, Jimei University, Xiamen, China 2 School of Software Engineering, Tongji University, Shanghai, China 3 Jiangxi University of Science and Technology, Nanchang, China 4 Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA 5 Faculty of Computer Science, University of Sunderland, Sunderland, UK 6 School of Informatics, Xiamen University, Xiamen, China 7 Department of Computer Science, Tongji University, Shanghai, China 8 College of Electronics and Information Engineering, Tongji University, Shanghai, China Correspondence should be addressed to Guokai Zhang; zhangguokai_01@163.com and Haiping Ren; chinarhp@163.com Received 8 February 2020; Accepted 6 March 2020; Published 1 April 2020 Guest Editor: Yi-Zhang Jiang Copyright©2020YanqiuZengetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. is paper puts forward a hybrid denoisingalgorithmforMRimagesbasedontwosparselyrepresentedmorphologicalcomponentsandoneresidualpart.Tobegin with,decomposeanoisyMRimageintothecartoon,texture,andresidualpartsbyMCA,andtheneachpartisdenoisedbyusing Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. e experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone. 1. Introduction Magnetic resonance imaging (MRI) is one of the advanced imageological examination methods for modern medicine. MRI uses powerful magnets and computer-generated radio waves instead of injected contrast agents to create multidi- mensional images of human organs and tissues. It does not damage the body with ionizing radiation, so it is safer than emission computed tomography (ECT). For this reason, MRI is frequently used for imaging tests of the brain and spinal cord. However, compared with computed tomography (CT), MRimagehasalowerspatialresolution,longerscantime,and more artifacts. e longer the scanning time, the greater the thermal noise (a kind of Gaussian noise). Besides, medical images are always polluted by various noises during collection, transmission, and storage. e magnitude of MRI data in the presence of noise generally follows a Rician dis- tribution if acquired with single-coil systems [1]. Also, the Gaussian distribution can approximate Rician noise in high SNR (signal-to-noise ratio) regions [2]. Quite often, noise affecting the pixels in an image is Gaussian in nature and uniformly deters information pixels in the image [3]. MR image denoising, as an essential preprocessing step for MRI data processing, has been a hot topic in the related area. Many scholars and researchers have performed much workonimagedenoising.Variousimagedenoisingmethods can be broadly classified as five categories: spatial domain filtering, transform domain filtering, methods in other domains, sparse representation and dictionary learning methods, and hybrid methods [3]. Hindawi Computational and Mathematical Methods in Medicine Volume 2020, Article ID 1405647, 10 pages https://doi.org/10.1155/2020/1405647