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Reseach Article

A Combined Dual-Tree Complex Wavelet (DT-CWT) and Bivariate Shrinkage for Ultrasound Medical Images Despeckling

by Romain Mavudila Kongo, Mohammed Cherkaoui, Lhousaine Masmoudi, Najem Hassanain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 14
Year of Publication: 2012
Authors: Romain Mavudila Kongo, Mohammed Cherkaoui, Lhousaine Masmoudi, Najem Hassanain
10.5120/7698-1033

Romain Mavudila Kongo, Mohammed Cherkaoui, Lhousaine Masmoudi, Najem Hassanain . A Combined Dual-Tree Complex Wavelet (DT-CWT) and Bivariate Shrinkage for Ultrasound Medical Images Despeckling. International Journal of Computer Applications. 49, 14 ( July 2012), 42-49. DOI=10.5120/7698-1033

@article{ 10.5120/7698-1033,
author = { Romain Mavudila Kongo, Mohammed Cherkaoui, Lhousaine Masmoudi, Najem Hassanain },
title = { A Combined Dual-Tree Complex Wavelet (DT-CWT) and Bivariate Shrinkage for Ultrasound Medical Images Despeckling },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 14 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number14/7698-1033/ },
doi = { 10.5120/7698-1033 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:17.564948+05:30
%A Romain Mavudila Kongo
%A Mohammed Cherkaoui
%A Lhousaine Masmoudi
%A Najem Hassanain
%T A Combined Dual-Tree Complex Wavelet (DT-CWT) and Bivariate Shrinkage for Ultrasound Medical Images Despeckling
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 14
%P 42-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an efficient DT-CWT based method for medical ultrasound images despeckling is presented. The ultrasound images are often deteriorated by speckle noise, this noise is a random granular texture that obscures anatomy in ultrasound images and degrades the detectability of low-contrast lesions. Speckle noise occurrence is often undesirable, since it affects the tasks of human interpretation and diagnosis. Different from many other schemes with wavelet transform are used on one side in which the studies have dealt more with the standard DWT case. However, the Discrete Wavelet Transform (DWT) has some disadvantages that undermine its application in image processing. In this study we investigated a performances complex wavelet transform (DT-CWT) combined with Bivariate Shrinkage. The proposed method was tested on a noisy ultrasound medical image, and the restored images show a great effectiveness of DT-CWT method compared to the classical DWT.

References
  1. J. W. Goodman. 1985. Statiscal optics. Wiley-interscience, New york.
  2. Insana, M. F. , Wagner, R. F. , Garra, B. S. , Brown, D. G. , and Shawker, T. H. (1986). Analysis of ultrasound image texture via generalized rician statistics. Optical Engineering, 25(6):743-748
  3. Krissian, K. , Vosburgh, K. , Kikinis, R. , and Westin, C. -F. (2004). Anisotropic diffusion of ultrasound constrained by speckle noise model. Technical Report 0004, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Laboratory of Mathematics in Imaging. ISSN.
  4. Montagnat, J. , Sermesant, M. , Delingette, H. , Malandain, G. , and Ayache, N. (2003). Anisotropic filtering for model-based segmentation of 4d cylindrical echocardiographic images. Pattern Recognition Letters, 24(4-5):815-828.
  5. Duan, Q. , Angelini, E. D. , and Laine, A. (2004). Assessment of fast anisotropic diffusion and scan conversion of real-time three-dimensional spherical ultrasound data for visual quality and spatial accuracy. In SPIE International Symposium on Medical Imaging, volume 5370, pages 526{537.
  6. Gupta, S. , Chauhan, R. C. , and Sexana, S. C. (2001). Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Comput. , 42:189{192.
  7. Achim, A. , Bezerianos, A. , and Tsakalides, P. (2001). Novel bayesianmultiscale method for speckle removal in medical ultrasound images. IEEE Transactions on Medical Imaging, 20(8):772{783.
  8. N. G Kinsbury 1998. The dual-tree complex wavelet transform: a new technique for shift invariance and directiona filters. In Proceedings of IEEE Digital Signal Processing Workshop.
  9. A. Pizurica, W. Philips and al 2003 . A versatile wavelet domain noise filtration technique for medical imaging . IEEE Trans. Medical Imaging, vol. 22, n° 3, pp. 323-331,
  10. M. Unser and A. Aldroubi 1996. A review of wavelets in biomedical application',proc. Of the IEEE,vol. n°4,pp. 626-634.
  11. Mariana Carmen Nicole, LuminitaMoraru and lauraonose 2010 . Comparative Approach for speckle Reduction in Medical ultrasound Images. Vol. ROMANIAJ. BIOPHYS. ,Vo. 20,1. P. 13-21, BUCHAREST.
  12. N. G Kinsbury2000. A dual-tree complex wavelet transform with improved orthogonality and symmetry proprieties. In Proceedings of the EEE ,Int. Conf. on Image Proc. (ICIP).
  13. N. G Kinsbury 1999. Image processing with complex wavelets. Phil. Trans. Royal Society London.
  14. Goodman, J. W. (1968). Introduction to Fourier Optics. McGraw-Hill, San Francisco.
  15. Burckhardt, C. B. (1978). Speckle in ultrasound b-mode scans. IEEE Transactions on Sonics and ltrasonics, 25(1):1-6.
  16. Kao, C. , Pan, X. , Hiller, E. , and Chen, C. (1998). A bayesian approach for edge detection in medical ultrasound images. IEEE Transactions on Nuclear Science, 45(6):3089{3096
  17. Wagner, R. , Smith, S. W. , Sandrik, J. M. , and Lopez, H. (1983). Statistics of speckle in ultrasound b-scans. IEEE Transactions on Sonics and Ultrasonics, 30(3):156-163
  18. Dutt, V. and Greenleaf, J. (1996). Adaptive speckle reduction _lter for logcompressed b-scan images. IEEE Transactions on Medical Imaging, 15(6):802-813.
  19. Kaplan, D. and Ma, Q. (1994). On the statistical characteristics of the log compressed rayleigh signals: Theorical formulation and experimental results. J. Acoust. Soc. Amer. , 95:1396 - 1400.
  20. Evans, A. and Nixon, M. S. (1993). Temporal methods for ultrasound speckle reduction. In IEE Seminar on Texture analysis in radar and sonar, volume 1, pages 1-6.
  21. Cincotti, G. , Loi, G. , and Pappalardo, M. (2001). Frequency decomposition and compounding of ultrasound medical images with wavelet packets. IEEE Transactions on Medical Imaging, 20(8):764-771.
  22. Trahey, G. E. , Smith, S. W. , and von Ramm, . T. (1986). Speckle reduction in medical ultrasound via spatial compounding. In Proc. 14th SPIE on Medical Applications, pages 626-637.
  23. AlinAchim, P. Tsakalides, and A. Bezarianos 2001. NovelBayesian multiscale method for speckle removal in medical ul-trasound images. IEEE Trans. on Medical Imaging , vol. 20pp. 772–783.
  24. [Bouc. '01] Samuel Foucher, Gozé Bertin Bénié, Jean-Marc Boucher. January 2001. Multiscale MAP Filtering of SAR images. IEEE Transactions on Image Processing, vol. 10, no. 1, , 49-60.
  25. Ali SAMIR ,(2006). Speckle Reduction of ultrasound imaging using wavelet analysis. IMIBE.
  26. Caroline Chaux and Jean-Christophe pesquet 2005. Image Analysis Using a Dual-Tree M-Band Wavelet Transform. IEEE Transaction on image processing.
  27. JunmeiZhong and RuolaNingoctober 2005. Image Denoising based on wavelets and Multifractals for singularity detection. IEEE transaction on image processing, vol. 14, n0. 10.
  28. Mallat 1998. A wavelet tour of signal processing: San Diego, CA, USA : Academic Press.
  29. N. G Kinsbury1999. Image processing with complex wavelets. Phil. Trans. Royal Society London,.
  30. N. G Kinsbury 2000. A dual-tree complex wavelet transform with improved orthogonality and symmetry proprieties. In Proceedings of the EEE ,Int. Conf. on Image Proc. (ICIP).
  31. I. Selesnick,R. G. Baraniuk and N. G. Kingsbury2005. The dual-tree complex wavelet transform: IEE Signal Processing magazine, vol. 22,n°6,pp. 123-151.
  32. I. Selesnick and K. Li2003. video denoising using 2D and 3D dual-tree complex wavelet transforms. in wavelet applications in signal and and 3D dual-tree complex wavelet transforms. in wavelet applications in signal and image processing X(proc. SPIE 5207).
  33. L. Sendur and I. W. Selesnick2002. Bivariate shrinkage functions for wavelet-based de-noising exploiting interscale dependency. IEEE Trans. on Signal Processing. 50(11): 2744-2756, November.
  34. LaventSendur and Ivan Selesnick2002. Bivariate Shrinkage With Local Variance Estimation. IEEE SIGNAL PROCESSING LETTERS,DECEMBER, VOL. 9,No. 12.
  35. D L Donoho1995. De-noising by soft thresholding. IEE Trans. Info, Theory, 41(3),613-627,.
Index Terms

Computer Science
Information Sciences

Keywords

Medical image denoising Medical ultrasound speckle noise Dual-tree wavelet transform Complex wavelet Bivariate shrinkage