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Hybrid Filtering Approach for Retrieval of MRI Image

  • K. MuruganEmail author
  • V. P. Arunachalam
  • S. Karthik
Image & Signal Processing
  • 54 Downloads
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

The quality of Magnetic Resonance Images(MRI) are degraded by the various types of noises. In this paper, a Hybrid Multi-resolution filter for denoising the MRI images degraded by the Salt and Pepper noise is proposed and the wavelet transform is used to improve the resolution of the denoised image.. The Hybrid filter consist of three value weighted filter and similarity based filter. In three value weighted filter, a variable local window is applied to find the noisy pixels. By using the noise free pixels in that window, the noisy pixels are reconstructed using three value method. In similarity based filter, a variable local window is applied to reconstruct the noisy pixels. In that window, based on the similarity between the noisy pixel sequence and noise free pixels sequence are used to reconstruct the noisy pixel. At last wavelet transform is used to increase the resolution of the reconstructed image. The experimental results shows that the proposed filter denoises the image and improves the resolution when compared to the existing methods and produces the efficiency of about 98%.

Keywords

Image denoising Hybrid filter Weighted filter Similarity filter Multi-resolution 

Notes

Compliance with Ethical Standards

Research Involving Human Participants and/or Animals - Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

No humans are involved.

References

  1. 1.
    Gonzalez, R. C., and Woods, R. E., Digital image processing. Englewood Cliffs, NJ: Prentice-Hall, 2002.Google Scholar
  2. 2.
    Brownrigg, R. K., The weighted median filter. Commun. ACM 27(8):807–818, 1984.CrossRefGoogle Scholar
  3. 3.
    Chen, T., and Wu, H. R., Space variant median filters for the restoration of impulse noise corrupted images. IEEE Trans. Circ. Syst. II: Anal. Digit. Signal Process. 48(8):784–789, 2001.CrossRefGoogle Scholar
  4. 4.
    Kos, J., and Lee, Y. H., Center weighted median filters and their applications to image enhancement. IEEE Trans. Circ. Syst. 38(9):984–993, 1991.CrossRefGoogle Scholar
  5. 5.
    Cheng, C.-C.; Cheng, F.-C.; Lin, P.-H., and Huang, S.-C., A block-based switch median filter for removing high density salt-and-pepper noises. 2014 IEEE Int. Conf. Consum. Electron. Taiwan, 2014.Google Scholar
  6. 6.
    Sulaiman, S. N., Che Ishak, S. M., Isa, I. S., and Hamzah, N., De-noising of noisy MRI brain image using the switching-based clustering algorithm. 2014 IEEE Int. Conf. Contrl. Syst. Comput. Eng. (ICCSCE 2014), 2014.Google Scholar
  7. 7.
    Yuan, S. Q., and Tan, Y. H., Impulse noise removal by a global–local noise detector and adaptive median filter. Sign. Process. 86(8):2123–2128, 2006.CrossRefGoogle Scholar
  8. 8.
    Veerakumar, T., Esakkirajan, S., and Vennila, I., Salt and pepper noise removal in video using adaptive decision based median filter. 2011 Int. Conf. Multimed. Sign. Process. Commun. Technol., 2011.Google Scholar
  9. 9.
    Jiang, D.-S., Li, X.-B., Wang, Z.-L., and Liu, C., An efficient edge-preserving approach based on adaptive fuzzy switching median filter. 2011 Int. Conf. Qual. Reliab. Risk Mainten. Saf. Eng., 2011.Google Scholar
  10. 10.
    Srinivasan, K. S., and Ebenezer, D., A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Sign. Process. Lett. 14(3):189–192, 2007.CrossRefGoogle Scholar
  11. 11.
    Daiyan, G. M., and Mottalib, M. A. Removal of high density salt & pepper noise through a modified decision based median filter. 2012 Int. Conf. Inform. Electron. Vision (ICIEV), 2012.Google Scholar
  12. 12.
    Wang, S. S., and Wu, C. H., A new impulse detection and filtering method for removal of wide range impulse noises. Pattern Recogn. 42(9):2194–2202, 2009.CrossRefGoogle Scholar
  13. 13.
    Jayasree, M., and Narayanan, N. K., An efficient mixed noise removal technique from gray scale images using noisy pixel modification technique. 2015 Int. Conf. Commun. Signal Process. (ICCSP), 2015.Google Scholar
  14. 14.
    Yu, G., Qi, L., Sun, Y., and Zhou, Y., Impulse noise removal by a non- monotone adaptive gradient method. Sign. Process. 90(10):2891–2897, 2010.CrossRefGoogle Scholar
  15. 15.
    Zhang, X. M., and Xiong, Y. L., Impulse noise removal using directional weighted noise detector and adaptive weighted mean filter. IEEE Sign. Process. Lett. 16(4):295–298, 2009.CrossRefGoogle Scholar
  16. 16.
    Buades, A., Coll, B., and Morel, J. M., A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2):490–530, 2005.CrossRefGoogle Scholar
  17. 17.
    Zhang, X., and Zhan, Y., Decision based non local mens filter for removing impulse noise from digital images. Sign. Process. 2(93):517–524, 2013.CrossRefGoogle Scholar
  18. 18.
    Zhang, P., and Li, F., A new adaptive weighted mean filter for removing salt and pepper noise. IEEE Sign. Process. Lett. 21(10):1280–1283, 2014.CrossRefGoogle Scholar
  19. 19.
    Ching T. L., and Yuechen, Y., Removal of salt and pepper noise in corrupted image using three values weighted approach with variable window size. Pattern Recogn. Lett., 2016.  https://doi.org/10.1016/j.patrec.2016.06.026, Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window, 80, 188, 199.
  20. 20.
    Panetta, K., Squence to Sequnce similarity based filter for image denoising. IEEE Sens. J. 16(11), 2016.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECETamilnadu College of EngineeringCoimbatoreIndia
  2. 2.SNS College of TechnologyCoimbatoreIndia
  3. 3.Department of CSESNS college of TechnologyCoimbatoreIndia

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