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A New Contourlet-based Compression and Speckle Reduction Method for Medical Ultrasound Images

by Seyyed Hadi Hashemi-berenjabad, Ali Mahloojifar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 13
Year of Publication: 2013
Authors: Seyyed Hadi Hashemi-berenjabad, Ali Mahloojifar
10.5120/14178-2383

Seyyed Hadi Hashemi-berenjabad, Ali Mahloojifar . A New Contourlet-based Compression and Speckle Reduction Method for Medical Ultrasound Images. International Journal of Computer Applications. 82, 13 ( November 2013), 26-32. DOI=10.5120/14178-2383

@article{ 10.5120/14178-2383,
author = { Seyyed Hadi Hashemi-berenjabad, Ali Mahloojifar },
title = { A New Contourlet-based Compression and Speckle Reduction Method for Medical Ultrasound Images },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 13 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number13/14178-2383/ },
doi = { 10.5120/14178-2383 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:57:39.896377+05:30
%A Seyyed Hadi Hashemi-berenjabad
%A Ali Mahloojifar
%T A New Contourlet-based Compression and Speckle Reduction Method for Medical Ultrasound Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 13
%P 26-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a threshold based method for speckle reduction and image compression of medical ultrasound images was presented. First, two ultrasound medical image despeckling methods were compared: wavelet-based and contourlet-besd, to find the best. Different measures were used for performance comparison and these methods were implemented both on synthesized data and real ultrasound images. It is found that, performance of the both techniques vary with the level of the speckle noise, and in the case of preserving image details and edges which is very important for medical image processing, contourlet-based method shows better performance over wavelet-based speckle reduction specially in high levels of noise. Then, a new contourlet-based lossy image compression method was presented for medical ultrasound images. In this algorithm, contourlet transform was used for image decomposition. Then, a new thresholding process was applied on the coefficients before quantization. The compression threshold was elected due to coefficients occurrence in the contourlet domain. This algorithm has the ability of simultaneous speckle reduction using another thresholding. Due to this time saving ability, the algorithm can be used in online image transmission systems. The proposed method was implemented on a real ultrasound images and ultrasound phantom image. Results proved that our proposed method has acceptable and good performance over common compression methods such as wavelet-based SPIHT in the case of PSNR.

References
  1. R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, 1983. "Statistics of speckle in ultrasound B-scans," IEEE Trans. Ultrasonics. , vol. 30, pp. 156-163, (May 1983).
  2. O. V. Michailovich and A. Tannenbaum, 2006. "Despeckling of Medical Ultrasound Images," IEEE Trans. Ultrasonics. , vol. 53, no. 1, (Jan. 2006).
  3. J. S. Lee, 1981 "Speckle analysis and smoothing of synthetic aperture radar images," Comput. Graph. Image Processing, vol. 17, pp. 24-32, (1981).
  4. J. S. Lee, 1980 "Digital image enhancement and noise filtering by using local statistics," IEEE Trans. Pattern Anal. Machine Intell. , vol. PAMI-2, no. 2, pp. 165-168, (1980).
  5. D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. chavel, 1987. "Adaptive restoration of images with speckle," IEEE Trans. Acoust. Specch Signal Processing, vol. ASSP-35, pp. 373-383, (1987).
  6. V. S. Frost, J. A. Stiles, K. S. Shanmuggam, and J. C. Holtzman 1982 "Amodel for radar images and its application for adaptive digital filtering of multiplicative noise," IEEE Trans. Pattern Anal. Machine Intell. , vol. 4, no. 2, pp. 157-165,(1982).
  7. P. A. Juang and M. N. Wu, 2007. "Ultrasound Speckle Image Process Using Wiener Pseudo-inverse Filtering," in Proc. 2007 The 33rd IEEE Industrial Electronics Society (IECON) Conf. , pp. 2446-2449.
  8. T. Huang, G. Yang, and G. Tang, 1979 "A fast two-dimensional median filtering algorithm," IEEE Trans. Acoust. Speech Signal Processing, vol. 27, no. 1, pp. 13-18, (1979).
  9. Lopes, E. Nezry, R. Touzi and H. Laur, 1990. "Maximum A Posteriori speckle filtering and first order texture models in SAR images," in Proc. 10th Geoscience and Remote Sensing Symposium, pp. 2409-2412, (May 1990).
  10. Y. Yongjian and S. T. Acton, 2002 "Speckle reducing anisotropic diffusion," IEEE Trams. Image Processing, vol. 11, no. 11, pp. 1260-1270, (Nov. 2002).
  11. J. S. Lee, 1981 "Refined filtering of image noise using local statistics," Comput. Graph. Image Processing, vol. 15, pp. 380-389, (1981).
  12. S. Zhong and V. Cherkassky, 2000. "Image denoising using wavelet thresholding and model selection," in Proc. IEEE Int. Conf. Image Processing, pp. 1-4, Nov. 2000.
  13. D. L. Donoho, 1995. "Denoising by soft thresholding," IEEE Trans. Inform. Theory, vol. 41, pp. 613-627, 1995.
  14. F. N. S. Medeiros, N. D. A. Mascarenhas, R. C. P. Marques, and C. M. Larprano, 2002 . "Edge preserving wavelet Speckle filtering," in Proc. 5th IEEE southwest Symp. Image Anal. Interpretation, pp. 281-285, April 7-9, 2002.
  15. M. N. Do, and M. Vetterli, 2005. " The Contourlet Transform: An Efficient Directional Multiresolution Image Representation," IEEE Trans. On Image Processing, vol. 14, no. 12, pp. 2091-2106, Dec. 2005.
  16. Parthiban, and R. Subramanian, 2006. " Speckle Noise Removal Using Contourlets," in Proc. International Conference on Information and Automation 2006, pp. 250-253.
  17. K. Jain, 1989. Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
  18. R. C. Gonzalez and Richard E. Woods, "Digital Image Processing", Second Edition, Pearson Education.
  19. G. K. Wallace, 1991 "The JPEG still picture compression standard," IEEE Transaction on Consumer Electronics, vol. 38, no. 1, pp. 30-44, (Feb. 1991).
  20. O. Rioul, and M. Vetterli, 1991"Wavelets and Signal Processing," IEEE Transaction on Signal Processing, vol. 8, no. 4, pp. 14-38. (1991).
  21. M. Shapiro, 1993. "Embedded Image Coding Using Zero trees of Wavelet Coefficients," IEEE Transaction on Signal Processing, vol. 41, no. 12, pp. 3445-3462, (1993).
  22. Said, and W. A. Pearlman, 1996"Anew Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees," IEEE Transaction on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 243-250, (1996).
  23. D. S. Taubman, 2000"High performance scalable image compression with EBCOT," IEEE Transaction on Image Processing, vol. 9, no. 7, pp. 1158-1170, (2000).
  24. Y. Fisher, 1995 "Fractal Image compression: Theory and Application", Springer Verlag, New York, (1995).
  25. M. N. Do, and M. Vetterli, 2005. "The Contourlet Transform: An Efficient Directional Multiresolution Image Representation," IEEE Trans. On Image Processing, vol. 14, no. 12, pp. 2091-2106, Dec. 2005.
  26. http://viva. ee. virginia. edu/downloads. html
  27. http://www. ee. nmt. edu/~erives/552_11/EE552. html
Index Terms

Computer Science
Information Sciences

Keywords

Contourlet Transform Compression Ratio Image Compression Speckle Reduction PSNR Ultrasound image Wavelet

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