Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

Restoration of Noisy Ultrasound Image with Rectilinear Effect using Curvelet

Print
PDF
International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 6
Year of Publication: 2011
Authors:
Komal Juneja
Akash Tayal
10.5120/2998-4028

Komal Juneja and Akash Tayal. Article: Restoration of Noisy Ultrasound Image with Rectilinear Effect using Curvelet. International Journal of Computer Applications 25(1):7-13, July 2011. Full text available. BibTeX

@article{key:article,
	author = {Komal Juneja and Akash Tayal},
	title = {Article: Restoration of Noisy Ultrasound Image with Rectilinear Effect using Curvelet},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {25},
	number = {1},
	pages = {7-13},
	month = {July},
	note = {Full text available}
}

Abstract

Ultrasound Imaging is primary modality in the diagnosis of many diseases. Compared to other imaging techniques ultrasound imaging owes its great popularity to the fact that it is safe and noninvasive procedure for visualizing the heart, vasculature, abdomen, fetal monitoring etc. Ultrasound images are degraded by intrinsic artifacts called speckle which is result of constructive and destructive coherent summation of ultrasound echoes. In ultrasound imaging if a relative motion exist (subject moves from its position) during imaging the recorded image will be blurred. This effect can be expressed by an impulse response in received echo pulses and respective image restoration is made possible by a post recording process. In this paper we proposed the method to remove the blurring and additive speckle noise is removed by curvelet transform domain which uses cycle spinning.

Reference

  • Ashish Thakur and R. S. Anand, “Feature Extraction in Ultrasound Medical Images” Ph.D. Thesis, IIT Roorkee, ID No Ph.D. 2006/AT/RSA/33, pp. 15-76.
  • S.C Som, J. Opt. Soc. Am. 61 (1971) pp. 859.
  • Coifman R R, Donoho D L. Translation invariant denoising. In Wavelets and Statistics, Springer Lecture Notes in Statis- tics 103, New York: Springer-Verlag, 1995, pp.125-150.
  • J. Starck, E. Cand_es, D. Donoho, The curvelet transform for image denoising, IEEE Trans.Image Process., 11, 670-684 (2002).
  • E. Cand_es, F. Guo, New multiscale transforms, minimum total variation synthesis: applications to edge- preserving image reconstruction, Signal Process., 82 (11),1519-1543 (2002).
  • J. Starck, F. Murtagh, E. Candues, F. Murtagh, D. Donoho, Gray and color image contrast enhancement by the curvelet transform, IEEE Trans. Image Process., 12 (6), 706-717 (2003).
  • J. Starck, E. Candues, D. Donoho, Astronomical image representation by the curvelet transform, Astronomy and Astrophysics, 398, 785-800 (2003).
  • M. Choi, R. Kim, M. Nam, H. Kim, Fusion of multispectral and panchromatic satellite images using the curvelet transform, IEEE Geosci. Remote Sensing Lett., 2 (2), 136-14 (2005).
  • E. Candues, D. Donoho, New tight frames of curvelets and optimal representations of objects with piecewise singularities, Comm. Pure Appl. Math., 57, 219-266 (2004).
  • J. Ma, A. Antoniadis, F.-X. Le Dimet, Curvelet-based multiscale detection and tracking for geophysical fuids, IEEE Trans. Geosci. Remote Sensing, 44 (12), 3626-3637 (2006).
  • J. Ma, Deblurring using singular integrals and curvelet shrinkage, Physics Letters A, 368, 245-250 (2007).
  • J. Ma, G. Plonka, Combined curvelet shrinkage and nonlinear anisotropic diffusion,IEEE Trans. Image Process., 16 (9), 2198-2206 (2007).
  • G. Plonka, J. Ma, Nonlinear regularized reaction-diffusion filters for denoising of images with textures, IEEE Trans. Image Process., 17 (8), 1283-1294 (2008).
  • J. Starck, M. Elad, D. Donoho, Image decomposition via the combination of sparse representation and a variational approach, IEEE Trans. Image Process., 14 (10), 1570-1582 (2005).
  • J. Bobin, J. Starck J. Fadili, Y. Moudden, D. Donoho, Morphological component analysis: an adaptive thresholding strategy, IEEE Trans. Image Process., 16 (11), 2675-2681 (2007).
  • B. Zhang, J. Fadili, J. Starck, Wavelets, ridgelets, and curvelets for Poisson noise removal, IEEE Trans. Image Process., 17 (7), 1093-1108 (2008).
  • C. Zhang, L. Cheng, Z. Qiu, L. Cheng, Multipurpose Water marking based on multiscale curvelet transform, IEEE Trans. Inform. Forensics and Security, 3 (4), 611- 619 (2008).
  • L. Jiang, X. Feng, H. Yin, Structure and texture image inpainting using sparse representations and an iterative curvelet thresholding approach, Wavelets,Multiresolution and Inform. Process., 6 (5), 691-705 (2008).
  • T. Geback, P. Koumoutsakos, Edge detection in microscopy images using curvelets, BMC Bioinformatics, 10 (75),1471-2105-10-75 ( 2009).
  • L. Tessens, A. Pizurica, A. Alecu, A. Munteanu, W. Philips, Context adaptive image denoising through modeling of curvelet domain statistics, J. Electronic Imaging, 17 (3), 03021:1-17(2008)
  • J. Meunier and M. Betrand, “Ultrasonic texture motion analysis: Theory and simulation,” IEEE Trans. Med. Imag., vol. 14,pp.-293-300, 1995.
  • Akash Tayal, Komal Juneja, “Ultrasound Image Restoration of Blurring Due to Rectilinear motion” proceedings of Third National Conference on Mathematical Techniques: Emerging Paradigms for Electronics and IT Industries (MATEIT)),India, pp. 6.3.1-6.3.4 ,2010
  • G. Beylkin, R. Coifman and V. Rokhlin. “Fast wavelet transforms and numerical algorithm”,Comm. on Pure and Appl. Math.44 (1991), pp.-141–183
  • G. Beylkin. “On the fast Fourier transform of functions with singularities” Appl. Comput Harmon.Anal., 2-4 (1995), 363-381.
  • E. J. Cand`es. “Harmonic analysis of neural networks” Applied and Computational HarmonicAnalysis 6 (1999), 197–218.
  • E. J. Cand`es and L. Demanet, “The curvelet representation of wave propagators is Optimally sparse” Pure Appl. Math., 58-11 (2005) 1472–1528.
  • E. J. Cand`es and L. Demanet. “Curvelets and fast wave equation solvers” Technical report, California Institute of Technology, 2005
  • Candµes E, Donoho D. Curvelets: A Surprisingly Effective Nonadaptive Representation of Objects with Edges.Curves and Surface, Nashville: Vanderbilt University Press, USA, 1999, pp.123-143
  • Feng Peng, Mi Deling, Pan Yingjun, Wei Biao and Jin Wei, “Noise Removal Approach using Curvelet Transform”, Opto- Electronic Engineering, 2005, 32(9), pp. 67-70
  • M. Young, Candes E J, Demanet L, Donoho D L, et al. “Fast Discrete Curvelet Transforms”, Applied and Computational Mathematics, California Institute of Technology, 2005.
  • Liang Dong, Shen Min, Gao Qingwei, Bao Wenxia and Qu, Lei, “A Method for Image De-noising Based on the Contourlet Transform Using Recursive Cycle Spinning”, Chinese Journal of Electronics,2005, 33(11), pp. 2044- 2046.
  • L. Boubchir and M.J Fadili, “Multivariate Statistical Modeling of Images with the Curvelet Transform”, in Proc. 8th International Conference on Signal Proc. And Its Applications, 2005, pp. 747-750.
  • Nquyen Truong T, “Multiresolution Direction Filterbanks: Theory, Design, and Applications”, IEEE Transactions on Signal Processing, 2005,53(101):3895-3905.
  • Langis Ganon “ Wavelet Filtering of Speckle Noise-Some Numerical Result,” Proceeding of the Conference Vision Interface 1999 , Trois-Riveres
  • J. C. Bamber and C. Daft. Adaptive filtering for reduction of speckle in ultrasonic pulse echo images. Ultrasonics, pages 41-44, 1986.