{"title":"Speckle Reducing Contourlet Transform for Medical Ultrasound Images","authors":"P.S. Hiremath, Prema T. Akkasaligar, Sharan Badiger","volume":56,"journal":"International Journal of Computer and Information Engineering","pagesStart":932,"pagesEnd":940,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/6361","abstract":"Speckle noise affects all coherent imaging systems\r\nincluding medical ultrasound. In medical images, noise suppression\r\nis a particularly delicate and difficult task. A tradeoff between noise\r\nreduction and the preservation of actual image features has to be made\r\nin a way that enhances the diagnostically relevant image content.\r\nEven though wavelets have been extensively used for denoising\r\nspeckle images, we have found that denoising using contourlets gives\r\nmuch better performance in terms of SNR, PSNR, MSE, variance and\r\ncorrelation coefficient. The objective of the paper is to determine the\r\nnumber of levels of Laplacian pyramidal decomposition, the number\r\nof directional decompositions to perform on each pyramidal level and\r\nthresholding schemes which yields optimal despeckling of medical\r\nultrasound images, in particular. The proposed method consists of the\r\nlog transformed original ultrasound image being subjected to contourlet\r\ntransform, to obtain contourlet coefficients. The transformed\r\nimage is denoised by applying thresholding techniques on individual\r\nband pass sub bands using a Bayes shrinkage rule. We quantify the\r\nachieved performance improvement.","references":"[1] Paul Suetens, Fundamentals of Medical Imaging , 1st Edition, Cambridge\r\nUniversity, U.K., pp.145-182, 2002.\r\n[2] N.K.Ragesh, A.R.Anil and R.Rajesh, Digital Image Denoising in Medical\r\nUltrasound images: A Survey, ICGST AIML-11 Conference, Dubai,\r\nUAE, pp.67-73, 12-14 April 2011.\r\n[3] S. Kalaivani Narayanan and R.S.D.Wahidabanu,A View of Despeckling\r\nin Ultrasound Imaging. Int.J.of Signal Processing, Image processing and\r\nPattern Recognition, Vol.2,No.3, pp.85-98,2009.\r\n[4] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Visual Enhancement\r\nof Digital Ultrasound Images using Multiscale Wavelet\r\nDomain, Int. J. of Pattern Recognition and Image Analysis , Vol.20,No.3,\r\npp.303-315, 2010.\r\n[5] M.N. Do and M. Vetterli, The Contourlet Transform: an Efficient\r\nDirectional Multiresolution Image Representation, IEEE Transactions\r\non Image Processing, Vol.14, No.12, pp.20912106, 2005.\r\n[6] S.Satheesh and KVSVR Prasad, Medical Image Denoising Using Adaptive\r\nThreshold Based On Contourlet Transform, Int.J.Advanced Computing,\r\nVol.2, No.2, pp.52-58, March 2011.\r\n[7] G.Balaji,Image Denoising using Contourlet Transform, RSM Int. J. of\r\nET & M , Vol.1, pp.40-46,July 2009.\r\n[8] Mao-yu-Huang, yueh-Min Huang and Ming-Shi Wang , Speckle Reduction\r\nof Ultrasound Image Based on Contourlet Transform, Int. Computer\r\nSymposium , Taipei, Taiwan. pp.178-182, Dec. 15-17, 2004.\r\n[9] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Despeckling\r\nMedical Ultrasound Images Using the Contourlet Transform, In: Proceedings\r\nof the 4th AMS Indian International Conference on Artificial\r\nIntelligence Tumkur, Karnataka,India, pp.1814-1827,16-18 Dec 2009.\r\n[10] P.S.Hiremath and Jyothi R.Tegnoor, Automatic Detection of Follicles\r\nin Ultrasound Images of Ovaries, In: Prococeedings of the 2nd International\r\nConf. on Cognition and Recognition, pp.468-473, April 10-12,\r\n2008.\r\n[11] P.S.Hiremath and Jyothi R. Tegnoor. Contourlet based Method for\r\nFollicle Detection in Ultrasound Images of Ovaries In:proceedings of the\r\nNational Seminar on Recent Treands in Image Processing and Pattern\r\nRecognition, pp.114-120, Feb. 15-16, 2010.\r\n[12] P.S.Hiremath and Jyothi R. Tegnoor, Automatic Detection of Follicles in\r\nUltrasound Images of Ovaries using Edge Based Method, IJCA Special\r\nIssue on Recent Trends in Image Processing and Pattern Recognition\r\npp.120-125, 2010.\r\n[13] C.B. Burckhardt, Speckle in ultrasound B-mode scans, IEEE Transactions\r\non Sonics Ultrasonics. Vol.25, No.1, pp.1-6, 1978.\r\n[14] R.F. Wagner, S.W. Smith, J.M. Sandrik, H. Lopez.Statistics of Speckle\r\nin Ultrasound B-scans, IEEE Transactions on SonicsUltrasonics, Vol.30,\r\npp.156-163, 1983.\r\n[15] J.W. Goodman, Some Fundamental Properties of Speckle, Optics. Soc.\r\nAm. Vol.66 No.11,pp.1145-1149, 1976.\r\n[16] J. C. Bamber and C. Daft Adaptive Filtering for Reduction of Speckle\r\nin Ultrasound Pulse-Echo Images, Ultrasonics pp.41-44. 1986.\r\n[17] V. Dutta,Statistical Analysis of Ultrasound Echo Envelope, Ph.D. dissertation,\r\nMayo Graduate School,Rochester, MN,1995.\r\n[18] Minh N. Do and Martin Vetterli. Contourlets, In: Beyond Wavelets,\r\nG.V.Well, J Stoeckerand, Academic Press,pp.1-27, 2003.\r\n[19] M.N.Do and M. Vetterli. Framming Pyramids, IEEE Transactions on\r\nSignal Processing , pp. 2329-2342, 2003.\r\n[20] M.N. Do, Contourlets: a new Directional Multiresolution Image Representation,\r\nConf. Signals Syst. Computer, Vol.1 pp.497501, 2002.\r\n[21] P.J.Burt and E.H. Adelson,The Laplacian Pyramid as a Compact Image\r\nCode, IEEE Transactions on communication Vol.31, pp. 532-540, 1983.\r\n[22] S. Chang, B. Yu and M. Vetterli, Adaptive Wavelet Thresholding\r\nfor Image Denoising and Compression, IEEE Transactions on Image\r\nProcessing, Vol. 9, No. 9, pp. 1532-1546, 2000.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 56, 2011"}