Abstract
Noise artifacts introduced in Magnetic Resonance Imaging (MRI) due to imperfection in radio-frequency coils typically deteriorate the performance of automated analysis of a brain MRI. Thus, it is necessary to eliminate the noise for effective analysis of MRI. The denoising techniques that work on pixels vicinity (e.g., Averaging filter and Gaussian filter) suffer in denoising due to loss of salient information in brain MRI. As a result, these techniques gain poor Peak Signal-to-Noise Ratio (PSNR) in denoising of an MRI. However, the methods that denoise an image based on non-local correlation of patches show significant improvement in the performance. In this work, we proposed an improved denoising method for brain MRI based on non-local correlation of patches. The input image is decomposed into two components: periodic component and smooth component using Fast Fourier Transform (FFT). These two components of the image are denoised separately using non-local based averaging. The filtered image is obtained by reconstructing these two components. Quantitative and qualitative results on a T1-w simulated brain MRI dataset and a local brain MRI dataset indicate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Magnetic resonance imaging. https://www.radiologyinfo.org/, Accessed 18 Aug 2018
Laha, M., Tripathi, P.C., Bag, S.: A skull stripping from brain MRI using adaptive iterative thresholding and mathematical morphology. In: International Conference on Recent Advances in Information Technology, pp. 1–6 (2018)
Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A.: Nonlinear anisotropic filtering of MRI data. IEEE Trans. Med. Imaging 11(2), 221–232 (1992)
Ashburner, J., Friston, K.J.: Voxel-based morphometry the methods. Neuroimage 11(6), 805–821 (2000)
Samsonov, A.A., Johnson, C.R.: Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels. Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med. 52(4), 798–806 (2004)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Computer Vision and Pattern Recognition, pp. 60–65 (2005)
Manjón, J.V., Carbonell-Caballero, J., Lull, J.J., García-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using non-local means. Med. Image Anal. 12(4), 514–523 (2008)
Chen, G., Zhang, P., Wu, Y., Shen, D., Yap, P.-T.: Collaborative non-local means denoising of magnetic resonance images. In: International Symposium on Biomedical Imaging (ISBI), pp. 564–567 (2015)
Moisan, L.: Periodic plus smooth image decomposition. J. Math. Imaging Vis. 39(2), 161–179 (2011)
Acknowledgements
The authors would like to acknowledge Asian Dwaraka Jalan hospital, Dhanbad for providing MRI dataset to accomplish this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sarkar, S., Tripathi, P.C., Bag, S. (2020). An Improved Non-local Means Denoising Technique for Brain MRI. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_66
Download citation
DOI: https://doi.org/10.1007/978-981-13-9042-5_66
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9041-8
Online ISBN: 978-981-13-9042-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)