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Optimization of Nonlocal Means Filtering Technique for Denoising Magnetic Resonance Images: A Review

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 436))

Abstract

Magnetic resonance images are affected by noise of various types, which provide a hindrance to accurate diagnosis. Thus, noise reduction is still an important and difficult task in case of MRI. The objective behind denoising of images is to effectively decrease the unwanted noise by retaining the image features. Many techniques have been proposed for denoising MR images, and each technique has its own advantages and drawbacks. Nonlocal means (NLM) is a popular denoising algorithm for MR images. But it cannot be applied in its original form to different applications. The goal of this paper is to present the various optimization techniques for NLM filtering approach to reduce the noise present in MRIs. The original NLM filters along with its various advancements and mathematical models have been included.

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References

  1. Zhu, H., Li, Y., Ibrahim, J.G., Shi, X., An, H., Chen, Y., Gao, W., Lin,W., Rowe,D.B., Peterson, B.S.: Regression models for identifying noise sources in magnetic resonance imaging. J. Am. Stat. Assoc. 104, 623–637 (2009)

    Google Scholar 

  2. Mohan, J., Krishnaveni, V., Guo. Y.: A survey on the magnetic resonance image denoising methods: Biomed. Signal Process. Control 9, 56–69 (2014)

    Google Scholar 

  3. Daessle, N.W., Prima, S., Coupe, P., Morrissey, S.P., Barillot, C.: Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. In: 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 171–179 (2008)

    Google Scholar 

  4. Gudbjartsson, H., Patz, S.: The rician distribution of noisy mri data. Magn. Reson. Med. 34, 910–914 (1995)

    Google Scholar 

  5. Macovski, A.: Noise in MRI. Magn. Reson. Med. 36, 494–497 (1996)

    Google Scholar 

  6. Buades, A., Coll, B., Morel J.M.: A non local algorithm for image denoising. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 60–65. IEEE Press, San Diego (2005)

    Google Scholar 

  7. Buades, A., Coll, B., Morel J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4, 490–530 (2005)

    Google Scholar 

  8. Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)

    Google Scholar 

  9. Mahmoudi, M., Sapiro, G.: Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process. Lett. 12, 839–842 (2005)

    Google Scholar 

  10. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. intell. 12(7), 629–639 (1990)

    Google Scholar 

  11. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)

    Google Scholar 

  12. Zhang, X., Hou, G., Ma,J., Yan, W., Lin, B.: Denoising MR images using non-local means filter with combined patch and pixel similarity. PLoS ONE 9 (2014)

    Google Scholar 

  13. Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A.: Nonlinear anisotropic filtering of MRI data. IEEE Trans. Med. Imaging 11(1), 221–232 (1992)

    Google Scholar 

  14. Scheunders, P., Backer, S.D.: Wavelet denoising of multicomponent images using a Gaussian scale mixture model and a noise-free image as priors. IEEE Trans. Image Process. 16(7), 1865–1872 (2007)

    Google Scholar 

  15. Thacker, N.A., Pokri, M.: Noise filtering and testing for MR using a multi-dimensional partial volume model. In: Proceedings of the Medical Image Understanding and Analysis, pp. 21–24 (2004)

    Google Scholar 

  16. Manjon, J.V., Thacker, N.A., Lull, J.J., Marti, G.G., Bonmati, L.M., Robles, M.: Multicomponent MR image denoising. Int. J. Biomed. Imaging (2009). doi:10.1155/2009/756897

    Google Scholar 

  17. Gal, Y., Mehnert, A.J.H., Andrew, P.B., B, Macmohan, K., Kennedy, D., Crozier, S.: Denioising of dynamic contrast enhanced MR images using dynamic non local means. IEEE Trans. Med. Imaging 29(2), 302–310 (2010)

    Google Scholar 

  18. Manjon, J., Coupe, P., Bonmati, L.M., Collins, D.L., Robles, M.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)

    Google Scholar 

  19. Buades, A., Coll, B., Morel, J.M.: Nonlocal image and movie denoising. Intern. J. Comput. Vis. 76, pp. 123–139 (2008)

    Google Scholar 

  20. Hu, J., Pu, Y., Wu, X., Zhang, Y., Zhou, J.: Improved DCT-based nonlocal means filter for MR images denoising. Comput. Math. Methods Med. (2012). doi:10.1155/2012/232685

    Google Scholar 

  21. Manjon, J.V., Caballero, J.C., Lull, J.J., Marti, G.G., Bonmati, L.M., Robles, M.: MRI denoising using nonlocal means. Med. Image Anal. 12(4), 514–523 (2008)

    Google Scholar 

  22. Vega, A.T., Perez, V.G., Fernandez, S.A., Westin, C.F.: Efficient and robust nonlocal means denoising of MR data based on salient features matching. J. Comput. Methods Program Biomed. 105(2), 131–144 (2012)

    Google Scholar 

  23. Kang, B., Choi, O., Kim, J.D., Hwang, D.: Noise reduction in magnetic resonance images using adaptive non local means filtering. Elect. Lett. 49(5) (2013)

    Google Scholar 

  24. Aksam, I.M., Jalil, A., Rathore, S., Ali, A., Hussain, M.: Brain MRI denoising and segmentation based improved adaptive non-local means. Int. J. Imaging Syst. Technol. 23, 235–248 (2013)

    Google Scholar 

  25. Nowak, R.D.: Wavelet- based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Proc. 8(10), 1408–1419 (1999)

    Google Scholar 

  26. Guo, T., Liu, Q., Luo, J.: Filter bank based nonlocal means for denoising magnetic resonance images. J. Shanghai Jiaotong Univ. (Sci.) 19(1), 72–78 (2014)

    Google Scholar 

  27. Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional texton. Int. J. Comput Vis. 43, 29–44 (2001)

    Google Scholar 

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Authors and Affiliations

  1. Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Nikita Joshi

  2. Amity Institute of Information Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Sarika Jain

Authors
  1. Nikita Joshi
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  2. Sarika Jain
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Corresponding author

Correspondence to Nikita Joshi .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Chankyapuri, Rm 327, South Asian Univ, Akbar Bhawan, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Nikita Joshi, Sarika Jain (2016). Optimization of Nonlocal Means Filtering Technique for Denoising Magnetic Resonance Images: A Review. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_1

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  • DOI: https://doi.org/10.1007/978-981-10-0448-3_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0447-6

  • Online ISBN: 978-981-10-0448-3

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