Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Buhailiqiemu Awudong, Paerhati Yakupu, Jingwen Yan and Qi Li ()
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Buhailiqiemu Awudong: School of Computer Science and Technology, Changchun University of Science and Technology, 7089 Weixing Road, Changchun 130022, China
Paerhati Yakupu: School of Computer Science and Technology, Changchun University of Science and Technology, 7089 Weixing Road, Changchun 130022, China
Jingwen Yan: Department of Electronic Engineering, Shantou University, 243 Daxue Road, Shantou 515063, China
Qi Li: School of Computer Science and Technology, Changchun University of Science and Technology, 7089 Weixing Road, Changchun 130022, China

Mathematics, 2024, vol. 12, issue 5, 1-21

Abstract: The inevitable noise generated in the acquisition and transmission process of MRIs seriously affects the reliability and accuracy of medical research and diagnosis. The denoising effect for Rician noise, whose distribution is related to MR image signal, is not good enough. Furthermore, the brain has a complex texture structure and a small density difference between different parts, which leads to higher quality requirements for brain MR images. To upgrade the reliability and accuracy of brain MRIs application and analysis, we designed a new and dedicated denoising algorithm (named VST–MCAATE), based on their inherent characteristics. Comparative experiments were performed on the same simulated and real brain MR datasets. The peak signal-to-noise ratio (PSNR), and mean structural similarity index measure (MSSIM) were used as objective image quality evaluation. The one-way ANOVA was used to compare the effects of denoising between different approaches. p < 0.01 was considered statistically significant. The experimental results show that the PSNR and MSSIM values of VST–MCAATE are significantly higher than state-of-the-art methods ( p < 0.01), and also that residual images have no anatomical structure. The proposed denoising method has advantages in improving the quality of brain MRIs, while effectively removing the noise with a wide range of unknown noise levels without damaging texture details, and has potential clinical promise.

Keywords: MRIs denoising; variance-stabilizing transformation (VST); morphological component analysis (MCA); sparse representation; local adaptive thresholds (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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