title: Denoising of 3D magnetic resonance images by using higher-order singular value decomposition.
abstract: The denoising of magnetic resonance (MR) images is important to improve the inspection quality and reliability of quantitative image analysis. Nonlocal filters by exploiting similarity and/or sparseness among patches or cubes achieve excellent performance in denoising MR images. Recently, higher-order singular value decomposition (HOSVD) has been demonstrated to be a simple and effective method for exploiting redundancy in the 3D stack of similar patches during denoising 2D natural image. This work aims to investigate the application and improvement of HOSVD to denoising MR volume data. The wiener-augmented HOSVD method achieves comparable performance to that of BM4D. For further improvement, we propose to augment the standard HOSVD stage by a second recursive stage, which is a repeated HOSVD filtering of the weighted summation of the residual and denoised image in the first stage. The appropriate weights have been investigated by experiments with different image types and noise levels. Experimental results over synthetic and real 3D MR data demonstrate that the proposed method outperforms current state-of-the-art denoising methods.
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Zhongbiao
authors.initials: Z
authors.lastname: Xu
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Nan
authors.initials: N
authors.lastname: Jia
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: chenwf@fimmu.com.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: foree@163.com.
authors.forename: Yanqiu
authors.initials: Y
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio
keywords: Higher-order singular value decomposition; MR; Nonlocal methods; Sparseness; Volume data denoising