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Adaptive Nonlocal Filtering for Brain MRI Restoration

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Advances in Signal Processing and Intelligent Recognition Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 425))

Abstract

Brain magnetic resonance images (MRI) plays a crucial role in neuroscience and medical diagnosis. Denoising brain MRI images is an important pre-processing step required in many of the automatic computed aided-diagnosis systems in neuroscience. Recently, nonlocal means (NLM) and variants of these filters, which are widely used in Gaussian noise removal from digital image processing, have been adapted to handle Rician noise which occur in MRI. One of the crucial ingredient for the successful image filtering with NLM is the patch similarity. In this work we consider the use of fuzzy Gaussian mixture model (FGMM) for determining the patch similarity in NLM instead of the usual Euclidean distance. Experimental results with different noise levels on synthetic and brain MRI images are given to highlight the advantage of the proposed approach. Comparison with other image filtering methods our scheme obtains better results in terms of peak signal to noise ratio and structure preservation.

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References

  1. Somasundram, K., Kalavathi, P.: Brain segmentation in magnetic resonance human head scans using multi-seeded region growing. Imaging Science Journal 62, 273–284 (2014)

    Article  Google Scholar 

  2. Kalavathi, P., Prasath, V.B.S.: Automatic segmentation of cerebral hemispheres in MR human head scans. International Journal of Imaging Systems and Technology - Neuroimaging and Brain Mapping (2015)

    Google Scholar 

  3. Kalavathi, P., Prasath, V.B.S.: Methods on skull stripping of MRI head scan images - A review. Journal of Digital Imaging (2015)

    Google Scholar 

  4. Prasath, V.B.S., Singh, A.: A hybrid convex variational model for image restoration. Appl. Math. Comput. 215, 3655–3664 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Prasath, V.B.S., Vorotnikov, D.: On a system of adaptive coupled PDEs for image restoration. Journal of Mathematical Imaging and Vision 48, 35–52 (2014)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  7. Prasath, V.B.S., Urbano, J.M., Vorotnikov, D.: Analysis of adaptive forward-backward diffusion flows with applications in image processing. Inverse Problems 31(105008), 30 (2015)

    Google Scholar 

  8. Prasath, V.B.S., Singh, A.: Well-posed inhomogeneous nonlinear diffusion scheme for digital image denoising. Journal of Applied Mathematics (2010)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Prasath, V.B.S., Vorotnikov, D., Pelapur, R., Jose, S., Seetharaman, G., Palaniappan, K.: Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent. IEEE Transactions on Image Processing 24, 5220–5235 (2015)

    Article  MathSciNet  Google Scholar 

  11. Gonzalez, R.C., Wood, R.: Digital image processing. Addison-Wesley, Reading (1993)

    Google Scholar 

  12. Buades, A., Coll, B., Morel, J.M.: A review of image denoising methods, with a new one. Multiscale Modeling and Simulation 4, 490–530 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  13. Prasath, V.B.S., Delhibabu, R.: Color image restoration with fuzzy Gaussian mixture model driven nonlocal filter. In: AIST, pp. 131–139 (2015)

    Google Scholar 

  14. Manjón, J.V., Carbonell-Caballero, J., Lull, J.J., Garciá-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using non-local means. Med. Image Anal. 12, 514–523 (2008)

    Article  Google Scholar 

  15. Coupé, 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 Transactions on Medical Imaging 27, 425–441 (2008)

    Article  Google Scholar 

  16. Zeng, J., Liu, Z.Q.: Type-2 fuzzy graphical models for pattern recognition. Studies in Computational Intelligence, vol. 666. Springer-Verlag, Berlin (2015)

    Google Scholar 

  17. Bilmes, J.A.: Gentle Tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models; Technical Report TR-97-021; International Computer Science Institute, Berkeley, CA, USA (1998)

    Google Scholar 

  18. Nabney, I.: NETLAB: Algorithms for pattern recognitions. Springer, London (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Imaging and Visualization Analysis(CiVA) Lab, Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA

    V. B. Surya Prasath

  2. Department of Computer Science and Applications, Gandhigram Rural Institute – Deemed University, Gandhigram, Dindigul, 624302, Tamil Nadu, India

    P. Kalavathi

Authors
  1. V. B. Surya Prasath
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  2. P. Kalavathi
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Corresponding author

Correspondence to V. B. Surya Prasath .

Editor information

Editors and Affiliations

  1. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. Department of Electrical, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Computer Engineering Department, Vladimir State University, Vladimir Region, Russia

    Sergey Mosin

  6. School of Electrical, Nanyang Technological University, Singapore, Singapore

    Maode Ma

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© 2016 Springer International Publishing Switzerland

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Prasath, V.B.S., Kalavathi, P. (2016). Adaptive Nonlocal Filtering for Brain MRI Restoration. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_48

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  • DOI: https://doi.org/10.1007/978-3-319-28658-7_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28656-3

  • Online ISBN: 978-3-319-28658-7

  • eBook Packages: EngineeringEngineering (R0)

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