We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
An Efficient Restoration Method for the Faint Dot Matrix Images

Ao Zhu Peng Cao
Random Articles
Reseach Article

Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold

by K. Karthikeyan, Dr. C. Chandrasekar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 9
Year of Publication: 2011
Authors: K. Karthikeyan, Dr. C. Chandrasekar
10.5120/2614-3646

K. Karthikeyan, Dr. C. Chandrasekar . Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold. International Journal of Computer Applications. 22, 9 ( May 2011), 8-14. DOI=10.5120/2614-3646

@article{ 10.5120/2614-3646,
author = { K. Karthikeyan, Dr. C. Chandrasekar },
title = { Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 9 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number9/2614-3646/ },
doi = { 10.5120/2614-3646 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:55.468970+05:30
%A K. Karthikeyan
%A Dr. C. Chandrasekar
%T Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 9
%P 8-14
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The main problem during diagnosis is the distortion of visual signals obtained which is due to the consequence of the coherent of nature of the wave transmitted. These distortions are termed as ‘Speckle Noise’. The present study focuses on proposing a technique to reduce speckle noise from ultrasonic devices. This technique uses a hybrid model that combines fourth order PDE based anisotropic diffusion, linked with SRAD filter and wavelet based BayesShrink technique. The proposed filter is compared with traditional filters and existing filters using anisotropic diffusion. Experimental results prove that the proposed method is efficient in reaching convergence quickly and producing quality denoised images.

References
  1. Abdullah, H.N., Hasan, M.F. and Tawfeeq, Q.S. (2008), Speckle noise reduction in SAR images using Double-Density Dual Tree DWT, Asian Journal of Information Technology, Vol. 7, No. 7, pp. 281-284.
  2. Andreu, F., Ballester, C., Caselles, V. and Mazn, J.M. (2001), Minimizing total variation flow, Differential and Integral Equations, Vol. 14, pp 321-360.
  3. Antoniadis, A. and Bigot, J. (2001), Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study, Journal of Statistical Software, Vol 6, No. I, P. 06.
  4. Arivazhagan, S., Deivalakshmi, S. and Kannan, K. (2007), Performance Analysis of Image Denoising System for different levels of Wavelet decomposition, International Journal of Imaging Science and Engineering (IJISE), Vol.1,No.3, pp. 104-107.
  5. Chang, G., Yu, B. and Vetterli, M. (2000),Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE Trans of Image Processing, Vol. 9, No. 9, pp. 1532-1546.
  6. Charbonnier, P., Aubert, G., Feraud, L.B. and Barlaud, M. (1994), Two deterministic half-quadratic regularization algorithms for computed imaging, IEEE International Conference on Image Processing, Vol. 2, pp. 168–172.
  7. Delakis, I., Hammad, O. and Kitney, R.I. (2007), Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI), Physics in Medicine and Biology, Vol. 52, No.13, pp. 3741.
  8. Donoho, D.L. and Johnstone, I.M. (1995), Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, Vol.90, No.432, pp.1200-1224.
  9. Donoho, D.L., Johnstone, I.M., Kerkyacharian, G. and Picard, D. (1995), Wavelet shrinkage: Asymptopic, Journal of the Royal Statistics Society, Series B, Vol. 57, pp. 301-369, 1995.
  10. Federico, A. and Kaufmann, G.H. (2007), Denoising in digital speckle pattern interferometry using wave atoms, Opt. Lett., Vol. 32, pp.1232-1234.
  11. Greer, J.B. and Bertozzi, A.L. (2004), Traveling Wave Solutions of Fourth Order PDEs for Image Processing, SIAM Journal on Mathematical Analysis, Vol. 36, pp. 38-68.
  12. Grieg, G., Kubler, O., Kikinis, R. and Jolesz, F.A. (1992), Nonlinear Anisotropic Filtering of MRI Data, IEEE Transactions on Medical Imaging, Vol. 11, No. 2, pp. 221-232.
  13. Hamza, A. B., Escamilla, P. L., Aroza, J. M. and Roldan, R. (1999), Removing Noise and Preserving Details with Relaxed Median Filters, Journal of Mathematical Imaging and Vision, pp. 161-177.
  14. Hossein, R., Mansur, V., Purang, A. and Saeed, G. (2008), Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors, EEE transactions on biomedical engineering , Vol. 55, No. 9, pp. 2152-2160.
  15. Kaur, A. and Singh, K. (2010), Speckle noise reduction by using wavelets, NCCI 2010 -National Conference on Computational Instrumentation CSIO, Chandigarh, INDIA, pp. 198-203.
  16. Keeling, S.L. and Stollberger, R. (2002), Nonlinear anisotropic diffusion filters for wide range edge sharpening, Inverse Problems, Vol. 18, pp 175-190.
  17. Lijima, T. (1962), Basic Theory on Normalization of Pattern”, Bulletin of the Electrotechnical Laboratory, Vol. 26, pp 368-388.
  18. Ling, J. and Bovik, A.C. (2002), Smoothing Low-SNR Molecular Images Via Anisotropic Median Diffusion, IEEE Transactions on Medical Imaging, Vol. 21, pp.377-384.
  19. Lysaker, M., Lundervold, A. and Tai, X.C. (2003), Noise Removal Using Fourth – Order Partial Differential Equation With Applications to Medical Magnetic Resonance Images in Space and Time, IEEE Trans. Image Processing, Vol. 12, pp. 1579-1590.
  20. Maini, R. and Aggarwal, H. (2010), A Novel Technique for Speckle Noise Reduction on Medical Images, International Journal of Applied Engineering Research, Vol. 5, No. 1, pp. 1-8.
  21. Mansourpour, M., Rajabi, M.A.A.R. and Blais, J.A. (2006), Effects and performance of speckle noise reduction filters on active radar and SAR images, Oral Presentation at the WG I/5 and I/6 Workshop on Topographic Mapping from Space, Ankara, Turkey.
  22. Mastriani, M. and Giraldez, A.E. (2006), Kalman’s Shrinkage for Wavelet-Based Despeckling of SAR Images, International Journal of Intelligent Systems and Technologies, Vol.1, No.3, pp.190-196
  23. Mateo, J.L. and Fernandez-Caballer, A. (2009), Finding out general tendencies in speckle noise reduction in ultrasound images, Expert Systems with Applications, Vol. 36, Issue 4, pp. 7786-7797.
  24. Min, L. and Xiangchu, F. (2007), Image restoration using total variation and anisotropic diffusion equation, Frontiers of Electrical and Electronic Engineering in China, Higher Education Press, co-published with Springer-Verlag GmbH, Vol. 2, No. 4, pp. 400-403.
  25. Mrazek, P., Weickert, J. and Steidl, G. (2003), Correspondence between Wavelet Shrinkage and Nonlinear Diffusion, Scale-Space 2003, LNCS 2695, pp. 101-116.
  26. Narayanan, S.K. and Wahidabanu, R.S.D. (2009), A View on Despeckling in Ultrasound Imaging, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 2, No.3, pp.198-203.
  27. Perona, P. and Malik, J. (1990), Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 12, pp 629-639.
  28. Pitas, I. and Venetsanopoulos, A.N. (1990), Nonlinear Digital Filters: Principles and Applications, Kluwer Academic Publishers, 1990.
  29. Puvanathasan, P. and Bizheva, K. (2007), Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set, Opt. Express, Vol. 15, Issue 24, pp. 15747-15758.
  30. Rajan, J. and Kaimal, M.R. (2006a), Image Denoising using Wavelet Embedded Anisotropic Diffusion (WEAD)”, Proceedings of IET International Conference on Visual Information Engineering pp 589-593.
  31. Rajan, J. and Kaimal, M.R. (2006b), Speckle reduction in Images with WEAD & WECD”, Computer Vision, Graphics and Image Processing, Lecturer Notes in Computer Science (LNCS) 4338, Springer –Verlag, pp. 184-193.
  32. Rajan, J., Kannan, K. and Kaimal, M.R. (2008), An Improved Hybrid Model for Molecular Image Denoising, Journal of Mathematical Imaging and Vision, Vol. 31 No. 1, pp. 73–79.
  33. Sudha, S., Suresh, G.R. and Sukanesh, R. (2009), Comparative Study on Speckle Noise Suppression Techniques for Ultrasound Images, International Journal of Engineering and Technology Vol. 1, No. 1, pp. 1793-8236.
  34. Sun, X. and Song, G. (2007), A New Anisotropic Diffusion Equation with Adaptive Fidelity Term, International Conference on Computational Intelligence and Security (CIS 2007), pp. 330-334.
  35. Thangavel, K., Manavalan, R. and Aroquiaraj, I.L. (2009), Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comparative Study, ICGST-GVIP Journal, ISSN 1687-398X, Volume (9), Issue (III), pp. 25-32.
  36. Torkamani-Azar, F. and Tait, K.E. (1996), Image recovery using the anisotropic diffusion equation, IEEE Transactions on Image Processing, Vol. 5, Issue 11, pp. 1573-1578.
  37. Wang, Z. and Zhang, D. (1999), Progressive switching Median filter for the removal of impulse noise from highly corrupted images, IEEE Transaction on circuits and Systems – II, Analog and Digital Signal Processing, Vol 46, No 1, pp.78-80.
  38. Wei. G.W. (1999), Generalized Perona-Malik equation for image processing, IEEE Signal Processing Letters,Vol. 6, pp. 165-167.
  39. You, Y., Tannenbaum, W.X. and Kaveh, A.M. (1996), Behavioral analysis of anisotropic diffusion in image processing, IEEE Transactions on Image Processing, Vol. 5, Issue 11, pp. 1539-1553.
  40. You, Y.L. and Kaveh, M. (2000), Fourth-Order Partial Differential Equations for Noise Removal, IEEE Trans. Image Processing, Vol. 9, pp. 1723-1730.
  41. Yu, Y. and Acton, S.T. (2002), Speckle Reducing Anisotropic Diffusion, IEEE Transactions on Image Processing, Vol. 11, No. 11, pp. 1260 – 1270.
Index Terms

Computer Science
Information Sciences

Keywords

Anisotropic Diffusion BayesShrink Fourth Order PDE Speckle denoising SRAD Filter Wavelet Based

Powered by PhDFocusTM