Speckle Reducing Contourlet Transform for Medical Ultrasound Images

Authors: P.S. Hiremath, Prema T. Akkasaligar, Sharan Badiger

Abstract:

Speckle noise affects all coherent imaging systems including medical ultrasound. In medical images, noise suppression is a particularly delicate and difficult task. A tradeoff between noise reduction and the preservation of actual image features has to be made in a way that enhances the diagnostically relevant image content. Even though wavelets have been extensively used for denoising speckle images, we have found that denoising using contourlets gives much better performance in terms of SNR, PSNR, MSE, variance and correlation coefficient. The objective of the paper is to determine the number of levels of Laplacian pyramidal decomposition, the number of directional decompositions to perform on each pyramidal level and thresholding schemes which yields optimal despeckling of medical ultrasound images, in particular. The proposed method consists of the log transformed original ultrasound image being subjected to contourlet transform, to obtain contourlet coefficients. The transformed image is denoised by applying thresholding techniques on individual band pass sub bands using a Bayes shrinkage rule. We quantify the achieved performance improvement.

Keywords: Contourlet transform, Despeckling, Pyramidal directionalfilter bank, Thresholding.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062772

References:

[1] Paul Suetens, Fundamentals of Medical Imaging , 1st Edition, Cambridge University, U.K., pp.145-182, 2002.
[2] N.K.Ragesh, A.R.Anil and R.Rajesh, Digital Image Denoising in Medical Ultrasound images: A Survey, ICGST AIML-11 Conference, Dubai, UAE, pp.67-73, 12-14 April 2011.
[3] S. Kalaivani Narayanan and R.S.D.Wahidabanu,A View of Despeckling in Ultrasound Imaging. Int.J.of Signal Processing, Image processing and Pattern Recognition, Vol.2,No.3, pp.85-98,2009.
[4] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Visual Enhancement of Digital Ultrasound Images using Multiscale Wavelet Domain, Int. J. of Pattern Recognition and Image Analysis , Vol.20,No.3, pp.303-315, 2010.
[5] M.N. Do and M. Vetterli, The Contourlet Transform: an Efficient Directional Multiresolution Image Representation, IEEE Transactions on Image Processing, Vol.14, No.12, pp.20912106, 2005.
[6] S.Satheesh and KVSVR Prasad, Medical Image Denoising Using Adaptive Threshold Based On Contourlet Transform, Int.J.Advanced Computing, Vol.2, No.2, pp.52-58, March 2011.
[7] G.Balaji,Image Denoising using Contourlet Transform, RSM Int. J. of ET & M , Vol.1, pp.40-46,July 2009.
[8] Mao-yu-Huang, yueh-Min Huang and Ming-Shi Wang , Speckle Reduction of Ultrasound Image Based on Contourlet Transform, Int. Computer Symposium , Taipei, Taiwan. pp.178-182, Dec. 15-17, 2004.
[9] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Despeckling Medical Ultrasound Images Using the Contourlet Transform, In: Proceedings of the 4th AMS Indian International Conference on Artificial Intelligence Tumkur, Karnataka,India, pp.1814-1827,16-18 Dec 2009.
[10] P.S.Hiremath and Jyothi R.Tegnoor, Automatic Detection of Follicles in Ultrasound Images of Ovaries, In: Prococeedings of the 2nd International Conf. on Cognition and Recognition, pp.468-473, April 10-12, 2008.
[11] P.S.Hiremath and Jyothi R. Tegnoor. Contourlet based Method for Follicle Detection in Ultrasound Images of Ovaries In:proceedings of the National Seminar on Recent Treands in Image Processing and Pattern Recognition, pp.114-120, Feb. 15-16, 2010.
[12] P.S.Hiremath and Jyothi R. Tegnoor, Automatic Detection of Follicles in Ultrasound Images of Ovaries using Edge Based Method, IJCA Special Issue on Recent Trends in Image Processing and Pattern Recognition pp.120-125, 2010.
[13] C.B. Burckhardt, Speckle in ultrasound B-mode scans, IEEE Transactions on Sonics Ultrasonics. Vol.25, No.1, pp.1-6, 1978.
[14] R.F. Wagner, S.W. Smith, J.M. Sandrik, H. Lopez.Statistics of Speckle in Ultrasound B-scans, IEEE Transactions on SonicsUltrasonics, Vol.30, pp.156-163, 1983.
[15] J.W. Goodman, Some Fundamental Properties of Speckle, Optics. Soc. Am. Vol.66 No.11,pp.1145-1149, 1976.
[16] J. C. Bamber and C. Daft Adaptive Filtering for Reduction of Speckle in Ultrasound Pulse-Echo Images, Ultrasonics pp.41-44. 1986.
[17] V. Dutta,Statistical Analysis of Ultrasound Echo Envelope, Ph.D. dissertation, Mayo Graduate School,Rochester, MN,1995.
[18] Minh N. Do and Martin Vetterli. Contourlets, In: Beyond Wavelets, G.V.Well, J Stoeckerand, Academic Press,pp.1-27, 2003.
[19] M.N.Do and M. Vetterli. Framming Pyramids, IEEE Transactions on Signal Processing , pp. 2329-2342, 2003.
[20] M.N. Do, Contourlets: a new Directional Multiresolution Image Representation, Conf. Signals Syst. Computer, Vol.1 pp.497501, 2002.
[21] P.J.Burt and E.H. Adelson,The Laplacian Pyramid as a Compact Image Code, IEEE Transactions on communication Vol.31, pp. 532-540, 1983.
[22] S. Chang, B. Yu and M. Vetterli, Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE Transactions on Image Processing, Vol. 9, No. 9, pp. 1532-1546, 2000.