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Linear Regression Model for Gaussian Noise Estimation and Removal for Medical Ultrasound Images

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 3
Year of Publication: 2012
Authors:
P. S. Hiremath
Prema T. Akkasaligar
Sharan Badiger
10.5120/7750-0808

P s Hiremath, Prema T Akkasaligar and Sharan Badiger. Article: Linear Regression Model for Gaussian Noise Estimation and Removal for Medical Ultrasound Images. International Journal of Computer Applications 50(3):11-15, July 2012. Full text available. BibTeX

@article{key:article,
	author = {P.s. Hiremath and Prema T. Akkasaligar and Sharan Badiger},
	title = {Article: Linear Regression Model for Gaussian Noise Estimation and Removal for Medical Ultrasound Images},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {3},
	pages = {11-15},
	month = {July},
	note = {Full text available}
}

Abstract

Ultrasound imaging is widely used in the field of medicine. It is used for imaging soft tissues in organs like liver, kidney, spleen, uterus, heart, brain etc. The common problem in ultrasound image is speckle noise which is caused by the imaging technique used, that may be based on coherent waves such as acoustic to laser imaging. The denoising is to be performed to improve the image quality for more accurate diagnosis. The objective of the paper is to propose a novel linear regression model for Gaussian representation of speckle noise in medical ultrasound images. The speckle noise is modelled as a Gaussian noise, with estimated mean and standard deviation based on PSNR of the ultrasound image, using the proposed linear model for Gaussian noise estimation and removal. The experimental results demonstrate the efficacy of the proposed method.

References

  • L J. W. Godman, 1976, Some Fundamental Properties of Speckle, J. Opt. Soc. Am. Vol. 66, No. 11, pp. 1145–1149.
  • C. B. Burckhardt, 1978, Speckle in Ultrasound B- Mode Scans, IEEE Trans. Sonics Ultrasonics, Vol. 25, pp. 1–6.
  • N. K. Ragesh, A. R. Anil and R. Rajesh, 2011, Digital Image Denoising in Medical Ultrasound images: A Survey, ICGST AIML-11 Conference, Dubai, UAE, 12-14 April 2011, pp. 67-73.
  • S. Kalaivani Narayanan and R. S. D. Wahidabanu, 2009, A View of Despeckling in Ultrasound Imaging. Int. Jl. of Signal Processing, Image Processing and Pattern Recognition, Vol. 2, No. 3 pp. 85-98.
  • A. Achim, P. Tsakalides and A. Bezarianos, 2001, Novel Bayesian Multi-Scale Method For Speckle Removal In Medical Ultrasound Images, IEEE Transactions on Medical Imaging, Vol. 20, No. 8, pp. 772–783.
  • X. Zong,A. F. Laine and E. A Geiser, 1998, Speckle Reduction and Contrast Enhancement of Echocardiograms via Multiscale Nonlinear Processing, IEEE Transactions on Medical Imaging,Vol. 17, pp. 532 -540.
  • P. S. Hiremath, Prema T. Akkasaligar and Sharan Badiger, 2010, Visual Enhancement of Digital Ultrasound Images using Multiscale Wavelet Domain, Int. Jl. of Pattern Recognition and Image Analysis, Vol. 10, No. 3, pp. 303-315.
  • M. N. Do and M. Vetterli, 2005, The Contourlet Transform: an Efficient Directional Multiresolution Image Representation, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091–2106.
  • P. S. Hiremath, Prema T. Akkasaligar and Sharan Badiger, 2011, Speckle Reducing Contourlet Transform for Medical Ultrasound Images, World Academy of Science, Engineering and Technology- Special Journal Issue, Vol. 80, August 2011, pp. 1217 – 1224.
  • P. S. Hiremath, Prema T. Akkasaligar and Sharan Badiger, 2011, Performance Comparison of Wavelet Transform and Contourlet Transform based methods for Despeckling Medical Ultrasound Images, International Journal of Computer Applications (0975 – 8887), Vol. 26 No. 9, July 2011, pp. 34-41.
  • P. Perona and J. Malik, 1990, Scale Space and Edge Detection using Anisotropic Diffusion, IEEE Trans. Pattern Anal. Machine Intell. , Vol. 12, pp. 629–639.
  • M. J. Black, Guillermo Sapiro, David Marimont, and David Heeger, 1998, Robust Anisotropic Diffusion, IEEE Trans. on Image Processing, Vol. 7, No. 3, pp. 421- 432.
  • M. J. Black, D. Fleet D. , and Y. Yacoob, 2000, Robustly Estimating Changes in Image Appearance, Int. Jl. Of Computer Vision and Image Understand, Vol. 78, pp. 8 -31.
  • Tamer Rabie, 2005, Robust Estimation Approach for Blind Denoising, IEEE Trans. on Image Processing, Vol. 14, No. 11, pp. 1755-1765.
  • S. Rajalaxmi, V. Arun Kumar, and P. Baskar, 2012, Window Based Linear Regression Filter for Echocardiographic Image Denoising, Int. Jl. of Systems Algorithms and Applications, Vol. 2, Issue ICRAET 12, May 2012, pp. 180-183.
  • P. S. Hiremath, Prema T. Akkasaligar and Sharan Badiger, 2010, The Cycle Spinning Based Contourlet Transform for Despeckling Medical Ultrasound Image, Proc. Int. Conference on Trends in Information Technology and Applications – 2010, Ajman, U. A. E. , December 2010, pp 72-76.