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Deringing of MRI medical images

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Abstract

A no-reference method to detect and suppress ringing effect in MRI images is suggested. The ringing detection method is based on finding the area where ringing effect is likely to appear and calculating the ratio of average edge-normal and edge-tangential derivatives moduli in this area. The area consists of pixels with the certain distance to basic edges—sharp edges that are distant from other edges. The proposed ringing suppression method is based on the projection onto the set of images with bounded total variation with ringing level control.

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References

  1. L. J. Erasmus, D. Hurter, M. Naudé, H. G. Kritzinger, and S. Acho, “A short overview of MRI artefacts,” South Afric. J. Radiol. 8(2), 13–17 (2004).

    Google Scholar 

  2. S. J. Lee, “An improved method for reduction of truncation artifact in magnetic resonance imaging,” Proc. SPIE: Appl. Digital Image Processing 4360, 587–598 (1998).

    Article  Google Scholar 

  3. C. W. Kok, Y. Hui, and T. Q. Nguyen “MRI truncation artifact reduction via wavelet shrinkage,” Proc. SPIE: Appl. Digital Image Processing 3078, 301–311 (1997).

    Article  Google Scholar 

  4. C. Jung and L. Jiao, “Novel Bayesian deringing method in image interpolation and compression using a SGLI prior,” Opt. Express 18(7), 7138–7149 (2010).

    Article  Google Scholar 

  5. M. Khambete and M. Joshi, “Blur and ringing artifact measurement in image compression using wavelet transform,” Proc. World Acad. Sci. Eng. Technol. 20, 183–186 (2007).

    Google Scholar 

  6. Bo-Xin Zuo, “Perceptual ringing metric to evaluate the quality of images restored using blind deconvolution algorithms,” Opt. Eng. 48(3), 037004 (2009).

    Article  Google Scholar 

  7. A. V. Nasonov and A. S. Krylov, “Scale-space method of image ringing estimation,” in Proc. Int. Conf. on Image Processing (ICIP09) (Cairo, 2009), pp. 2794–2797.

    Google Scholar 

  8. A. V. Nasonov and A. S. Krylov, “Adaptive image deringing,” in Proc. 19th Int. Conf. on Computer Graphics GraphiCon2009 (Moscow, 2009), pp. 151–154.

    Google Scholar 

  9. A. V. Nasonov and A. S. Krylov, “Basic edges metrics for image deblurring,” in Proc. 10th Conf. on Pattern Recognition and Image Analysis: New Information Technologies (St. Petersburg, 2010), Vol. 1, pp. 243–246.

    Google Scholar 

  10. R. Fabbri, L. da F. Costa, J. C. Torelli, and O. M. Bruno, “2D euclidean distance transforms: a comparative survey,” ACM Comput. Surv. 40(1) (2008).

    Google Scholar 

  11. S. Mallat, A Wavelet Tour of Signal Processing (Acad. Press, 1999).

    MATH  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russia, 119991, Moscow, Leninskie gory, Russia

    A. M. Yatchenko, A. S. Krylov & A. V. Nasonov

Authors
  1. A. M. Yatchenko
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  2. A. S. Krylov
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  3. A. V. Nasonov
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Corresponding author

Correspondence to A. S. Krylov.

Additional information

The work was supported by the Federal Targeted Program “R&D in Priority Fields of the S&T Complex of Russia 2007–2013.”

The article is published in the original.

Artem Mikhaylovich Yatchenko (born 1987), graduated from the Faculty of Computational Mathematics and Cybernetics in 2009, Lomonosov Moscow State University (MSU). Currently a postgraduate student of the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University (MSU).

His main research interests lie in mathematical methods of image processing, medical image processing.

Andrey Serdzhevich Krylov (born 1956), Graduated from the Faculty of Computational Mathematics and Cybernetics in 1978, Lomonosov Moscow State University (MSU). Received the degree of PhD in 1983, the degree of Dr. Sc. in 2009. He is professor and head of the Laboratory of Mathematical Methods of Image Processing at the Faculty of Computational Mathematics and Cybernetics, MSU.

His main research interests lie in mathematical methods of multimedia data processing and analysis.

Andrey Vladimirovich Nasonov (born 1985), Graduated from the Faculty of Computational Mathematics and Cybernetics in 2007, Lomonosov Moscow State University (MSU). Received the degree of PhD in 2011. Currently a member of a scientific staff of the Laboratory of Mathematical Methods of Image Processing at the Faculty of Computational Mathematics and Cybernetics, MSU.

His main research interests lie in variational methods of image processing, inverse and ill-posed problems.

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Yatchenko, A.M., Krylov, A.S. & Nasonov, A.V. Deringing of MRI medical images. Pattern Recognit. Image Anal. 23, 541–546 (2013). https://doi.org/10.1134/S1054661813040184

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  • DOI: https://doi.org/10.1134/S1054661813040184

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