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Denoising of brain magnetic resonance images using a MDB network

  • 1181: Multimedia-based Healthcare Systems using Computational Intelligence
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Abstract

The denoising of brain magnetic resonance images could be important for the medical image analysis. Many algorithms have been proposed for this task, especially the deep learning ones which show great success compared with the classical image processing algorithms. Though satisfied results it achieve, they may fail to consider the contextual and attentive features during the feature learning process, and those ones could provide essential and complementary information for the feature encoding, and the poor learning of them could hinder the model to achieve a better performance. To address this challenge, in this paper, we propose a multi-dilated block (MDB) which aims to extract more contextual and attentive features during the feature extraction stage. The whole network is based on DnCNN, and the MDB is placed in the middle stage of the network to learn the contextual and attentive representations. Moreover, for improving the similarity between the noisy image and the denoised one from feature-level, we propose a perceptual loss which is able to further boost the performance of the MDB network. To validate the effectiveness of our proposed method, we conduct extensive experiments on the brain magnetic resonance images to compare the peak signal to noise ratio and structural similarity index, and the final experimental results demonstrate that our propose method could predict a higher resolution image compared with other ones.

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References

  1. Ali HM (2018) MRI medical image denoising by fundamental filters. High-Resolution Neuroimaging-Basic Physical Principles and Clinical Applications 14:111–124

  2. Anand CS, Sahambi JS (2008) MRI denoising using bilateral filter in redundant wavelet domain. TENCON 2008-2008 IEEE Region 10 Conference. IEEE 1–6

  3. Ashtari M, Perrine K, Elbaz R et al (2005) Mapping the functional anatomy of sentence comprehension and application to presurgical evaluation of patients with brain tumor. Am J Neuroradiol 26(6):1461–1468

    Google Scholar 

  4. Balafar MA, Ramli AR, Saripan MI et al (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33(3):261–274

    Article  Google Scholar 

  5. Baselice F, Ferraioli G, Pascazio V (2017) A 3D MRI denoising algorithm based on Bayesian theory. Biomed Eng Online 16(1):1–19

    Article  Google Scholar 

  6. Bhujle HV, Chaudhuri S (2013) Laplacian based non-local means denoising of MR images with Rician noise. Magn Reson Imaging 31(9):1599–1610

    Article  Google Scholar 

  7. Bhujle HV, Vadavadagi BH (2019) NLM based magnetic resonance image denoising-A review. Biomed Signal Process Control 47:252–261

    Article  Google Scholar 

  8. Borges P, Sudre C, Varsavsky T et al (2019) Physics-informed brain MRI segmentation. International Workshop on Simulation and Synthesis in Medical Imaging. Springer, Cham 100−109

  9. Chang L, ChaoBang G, Xi Y (2015) A MRI denoising method based on 3D nonlocal means and multidimensional PCA. Comput Math Methods Med 2015

  10. Chang YN, Chang HH (2015) Automatic brain MR image denoising based on texture feature-based artificial neural networks. Biomed Mater Eng 26(s1):S1275–S1282

    Article  Google Scholar 

  11. Golshan HM, Hasanzadeh RP (2014) An optimized LMMSE based method for 3D MRI denoising. IEEE/ACM Trans Comput Biol Bioinform 12(4):861–870

    Article  Google Scholar 

  12. Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34(6): 910–914

    Article  Google Scholar 

  13. Guo S, Yan Z, Zhang K et al (2019) Toward convolutional blind denoising of real photographs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 1712–1722

  14. Han X, Fischl B (2007) Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans Med Imaging 26(4):479–486

    Article  Google Scholar 

  15. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770–778

  16. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  17. Li S, Zhou J, Liang D et al (2020) MRI denoising using progressively distribution-based neural network. Magn Reson Imaging 71:55–68

    Article  Google Scholar 

  18. Ma J, Plonka G (2007) Combined curvelet shrinkage and nonlinear anisotropic diffusion. IEEE Trans Image Process 16(9):2198–2206

    Article  MathSciNet  Google Scholar 

  19. Maggioni M, Katkovnik V, Egiazarian K et al (2012) Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process 22(1):119–133

    Article  MathSciNet  MATH  Google Scholar 

  20. Manjón JV, Carbonell-Caballero J, Lull JJ et al (2008) MRI denoising using non-local means. Med Image Anal 12(4):514–523

    Article  Google Scholar 

  21. Manjón JV, Coupé P, Buades A et al (2012) New methods for MRI denoising based on sparseness and self-similarity. Med Image Anal 16(1):18–27

    Article  Google Scholar 

  22. Manjón JV, Coupé P, Buades A (2015) MRI noise estimation and denoising using non-local PCA. Med Image Anal 22(1):35–47

    Article  Google Scholar 

  23. Marroquín J L, Vemuri B C, Botello S et al (2002) An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans Med Imaging 21(8):934−945

    Article  MATH  Google Scholar 

  24. Mohan J, Krishnaveni V, Guo Y (2013) MRI denoising using nonlocal neutrosophic set approach of Wiener filtering. Biomed Signal Process Control 8(6):779–791

    Article  Google Scholar 

  25. Muckley M J, Ades-Aron B, Papaioannou A et al (2021) Training a neural network for Gibbs and noise removal in diffusion MRI. Magn Reson Med 85(1):413–428

    Article  Google Scholar 

  26. Tian C, Xu Y, Fei L et al (2019) Enhanced CNN for image denoising. CAAI Trans Intell Technol 4(1):17–23

    Article  Google Scholar 

  27. Tian C, Xu Y, Li Z et al (2020) Attention-guided CNN for image denoising. Neural Netw 124:117–129

    Article  Google Scholar 

  28. Tripathi PC, Bag S (2020) CNN-DMRI: a convolutional neural network for denoising of magnetic resonance images. Pattern Recognit Lett 135:57–63

    Article  Google Scholar 

  29. Varadarajan D, Haldar JP (2015) A majorize-minimize framework for Rician and non-central chi MR images. IEEE Trans Med Imaging 34(10):2191–2202

    Article  Google Scholar 

  30. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122

  31. Zhang X, Xu Z, Jia N et al (2015) Denoising of 3D magnetic resonance images by using higher-order singular value decomposition. Med Image Anal 19(1):75–86

    Article  Google Scholar 

  32. Zhang K, Zuo W, Chen Y et al (2017a) Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142–3155

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhang K, Zuo W, Gu S et al (2017b) Learning deep CNN denoiser prior for image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 3929–3938

  34. Zhang K, Zuo W, Zhang L (2018) FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Trans Image Process 27(9):4608–4622

    Article  MathSciNet  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Guokai Zhang

  2. Software School of Xiamen University, Xiamen, Fujian, China

    Chenxi Huang

  3. Technology Transformation Center, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China

    Jingwen Jiang

  4. School of Software Engineering, Tongji University, Shanghai, China

    Weizhe Xu

  5. Department of Otolaryngology, Head & Neck Surgery, Shanghai Ninth People’s Hospital, affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China

    Jianqing Chen

  6. Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Shanghai, China

    Xiaowen Xu

Authors
  1. Guokai Zhang
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  2. Chenxi Huang
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  3. Jingwen Jiang
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  4. Weizhe Xu
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  5. Jianqing Chen
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  6. Xiaowen Xu
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Corresponding authors

Correspondence to Jianqing Chen or Xiaowen Xu.

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Zhang, G., Huang, C., Jiang, J. et al. Denoising of brain magnetic resonance images using a MDB network. Multimed Tools Appl 81, 41751–41763 (2022). https://doi.org/10.1007/s11042-021-11521-8

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  • DOI: https://doi.org/10.1007/s11042-021-11521-8

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