Skip to main content
SpringerLink
Log in

Adaptive Non-local Means Filtering for Speckle Noise Reduction

  • Conference paper
Computer Vision and Graphics (ICCVG 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8671))

Included in the following conference series:

Abstract

The aim of this study is to present the results of investigations concerning the evaluation of non-local means filter for multiplicative noise removal in ultrasonographic images. In this work a comparison of different techniques based on the concept of the non-local means filtering and a novel application for a filter called trimmed non-local means has been presented. The proposed modification is a generalization of the non-local means algorithm, in which the pixels are ordered using rank-ordered absolute differences statistic and only the most centrally located pixels in the filtering window are considered and used to calculate the weights needed for the averaging operation. The experiments confirmed that the proposed algorithm achieves comparable results with the existing state-of-the-art denoising schemes in suppressing multiplicative noise in ultrasound images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Achim, A., Bezerianos, A., Tsakalides, P.: Ultrasound image denoising via maximum a posteriori estimation of wavelet coefficients. In: Proc. of the 23rd Annual Intern. Conf. of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 2553–2556 (2001)

    Google Scholar 

  2. Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation 4(2), 490–530 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  3. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)

    Google Scholar 

  4. Buades, A., Coll, B., Morel, J.M.: Non-Local Means Denoising. Image Processing On Line 2011 (2011)

    Google Scholar 

  5. Coupe, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans. on Image Proc. 18(10), 2221–2229 (2009)

    Article  MathSciNet  Google Scholar 

  6. Deledalle, C.A., Denis, L., Tupin, F.: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. on Image Proc. 18(12), 2661–2672 (2009)

    Article  MathSciNet  Google Scholar 

  7. Garnett, R., Huegerich, T., Chui, C., He, W.: A universal noise removal algorithm with an impulse detector. IEEE Trans. on Image Proc. 14(11), 1747–1754 (2005)

    Article  Google Scholar 

  8. Hacini, M., Hachouf, F., Djemal, K.: A new speckle filtering method for ultrasound images based on a weighted multiplicative total variation. Signal Processing (2013)

    Google Scholar 

  9. Krissian, K., Kikinis, R., Westin, C.F., Vosburgh, K.: Speckle-constrained filtering of ultrasound images. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 547–552 (2005)

    Google Scholar 

  10. Loizou, C.P., Theofanous, C., Pantziaris, M., Kasparis, T.: Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery. Computer Methods and Programs in Biomedicine 114(1), 109–124 (2014)

    Article  Google Scholar 

  11. Loizou, C., Pattichis, C., Christodoulou, C., Istepanian, R.S.H., Pantziaris, M., Nicolaides, A.: Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control 52(10), 1653–1669 (2005)

    Article  Google Scholar 

  12. Loupas, T., McDicken, W., Allan, P.: An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. on Circuits and Systems 36(1), 129–135 (1989)

    Article  Google Scholar 

  13. Radlak, K., Smolka, B.: Trimmed non-local means technique for mixed noise removal in color images. In: IEEE International Symposium on Multimedia (ISM), pp. 405–406 (2013)

    Google Scholar 

  14. Slabaugh, G., Unal, G., Fang, T., Wels, M.: Ultrasound-specific segmentation via decorrelation and statistical region-based active contours. In: IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 45–53 (2006)

    Google Scholar 

  15. Smolka, B., Perczak, M.: Generalized vector median filter. In: 5th Inter. Symposium on Image and Signal Processing and Analysis, pp. 254–257 (2007)

    Google Scholar 

  16. Tao, Z., Tagare, H., Beaty, J.: Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE Trans. on Medical Imaging 25(11), 1483–1491 (2006)

    Article  Google Scholar 

  17. Wagner, R., Smith, S., Sandrik, J., Lopez, H.: Statistics of speckle in ultrasound b-scans. IEEE Trans. on Sonics and Ultrasonics 30(3), 156–163 (1983)

    Article  Google Scholar 

  18. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Proc. 13(4), 600–612 (2004)

    Article  Google Scholar 

  19. Wu, Y., Tracey, B., Natarajan, P., Noonan, J.: Probabilistic non-local means. IEEE Signal Processing Letters 20(8), 763–766 (2013)

    Article  Google Scholar 

  20. Yu, Y., Acton, S.: Speckle reducing anisotropic diffusion. IEEE Trans. on Image Proc. 11(11), 1260–1270 (2002)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Krystian Radlak & Bogdan Smolka

Authors
  1. Krystian Radlak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bogdan Smolka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences (SGGW), Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences - SGGW, Nowoursynowska str. 159, 02-776, Warsaw, Poland

    Ryszard Kozera

  3. The .enpeda.. Project, The University of Auckland, New Zealand

    Bok-Suk Shin

  4. Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Konrad Wojciechowski

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Radlak, K., Smolka, B. (2014). Adaptive Non-local Means Filtering for Speckle Noise Reduction. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11331-9_62

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11330-2

  • Online ISBN: 978-3-319-11331-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation