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Reseach Article

Denoising of Poisson and Rician Noise from Medical Images using Variance Stabilization and Multiscale Transforms

by V. Naga Prudhvi Raj, T. Venkateswarlu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 21
Year of Publication: 2012
Authors: V. Naga Prudhvi Raj, T. Venkateswarlu
10.5120/9238-3865

V. Naga Prudhvi Raj, T. Venkateswarlu . Denoising of Poisson and Rician Noise from Medical Images using Variance Stabilization and Multiscale Transforms. International Journal of Computer Applications. 57, 21 ( November 2012), 24-31. DOI=10.5120/9238-3865

@article{ 10.5120/9238-3865,
author = { V. Naga Prudhvi Raj, T. Venkateswarlu },
title = { Denoising of Poisson and Rician Noise from Medical Images using Variance Stabilization and Multiscale Transforms },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 21 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number21/9238-3865/ },
doi = { 10.5120/9238-3865 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:01:04.307078+05:30
%A V. Naga Prudhvi Raj
%A T. Venkateswarlu
%T Denoising of Poisson and Rician Noise from Medical Images using Variance Stabilization and Multiscale Transforms
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 21
%P 24-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital imaging in medicine is improving the medical standards since last few decades. The images acquired by various imaging modalities suffer from various kinds of noise in the acquisition phase. The noise in the image decrease the contrast of the image and it becomes difficult to locate the tumours, lesions etc from these corrupted images. So the removal of noise from these images is very important. In this paper we developed the algorithms for the removal of Poisson noise in X-Ray Images and Rician noise in Magnetic Resonance Images. The noise in these modalities won't follow the Gaussian distribution. The Poisson noise in X-ray images will follow the Poisson distribution and the noise in MR images is modeled as Rician noise. In this work we developed the algorithms using Discrete wavelet transform, Undecimated wavelet transform, Dual tree Complex wavelet transform, Double Density discrete wavelet transform and Double density dual tree complex wavelet transforms to decompose the image into multiple resolution levels along with the variance stabilisation transforms to convert the Poisson noise and Rician noise into approximate gaussian noise. The performance of the algorithms were evaluated using PSNR (Peak signal to noise ratio), UQI (Universal quality index) and SSIM (Structural similarity index) etc. The results show that the double density dual tree complex wavelet transform is performing well than the other transforms.

References
  1. J. W. Goodman, "Some fundamental properties of speckle," J. Opt. Soc. Am. , vol. 66, no. 11, pp. 1145-1149, 1976.
  2. C. B. Burckhardt, "Speckle in ultrasound B-mode scans," IEEE Trans. Sonics Ultrasonics, vol. SU-25, no. 1, pp. 1-6, 1978.
  3. Z. Tao, H. D. Tagare, and J. D. Beaty. Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE Transactions on Medical Imaging, 25(11):14831491, 2006.
  4. P. C. Tay, S. T. Acton, and J. A. Hossack. A stochastic approach to ultrasound despeckling. In Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on, pages 221224, 2006.
  5. J. S. Lee, "Digital image enhancement and noise filtering by using local statistics," IEEE Trans. Pattern Anal. Mach. Intell. , PAMI-2, no. 2, pp. 165-168, 1980.
  6. J. S. Lee, "Speckle analysis and smoothing of synthetic aperture radar images," Comp. Graphics Image Process. , vol. 17, pp. 24-32, 1981.
  7. J. S. Lee, "Refined filtering of image noise using local statistics," Comput. Graphics Image Process, vol. 15, pp. 380-389, 1981.
  8. V. S. Frost, J. A. Stiles, K. S. Shanmungan, and J. C. Holtzman, "A model for radar images and its application for adaptive digital filtering of multiplicative noise," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 4, no. 2, pp. 157- 165, 1982.
  9. D. T. Kuan and A. A. Sawchuk, "Adaptive noise smoothing filter for images with signal dependent noise," IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-7, no. 2, pp. 165- 177, 1985.
  10. D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive restoration of images with speckle," IEEE Trans. Acoust. , vol. ASSP-35, pp. 373-383, 1987, doi:10. 1109/TASSP. 1987. 1165131.
  11. J. Saniie, T. Wang, and N. Bilgutay, "Analysis of homomorphic processing for ultrasonic grain signal characterization," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 3, pp. 365-375, 1989, doi:10. 1109/58. 19177.
  12. A. Pizurica, A. M. Wink, E. Vansteenkiste, W. Philips, and J. Roerdink, "A review of wavelet denoising in mri and ultrasound brain imaging," Curr. Med. Imag. Rev. , vol. 2, no. 2, pp. 247-260, 2006.
  13. D. L. Donoho, "Denoising by soft thresholding," IEEE Trans. Inform. Theory, vol. 41, pp. 613-627, 1995.
  14. X. Zong, A. Laine, and E. Geiser, "Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing," IEEE Trans. Med. Imaging, vol. 17, no. 4, pp. 532-540, 1998.
  15. Paul Butler, "Applied Radiological Imaging for Medical Students", Ist Edition, Cambridge University Press, 2007.
  16. Rangaraj M. Rangayyan, "Biomedical Signal Analysis A Case study Approach", IEEE Press, 2005.
  17. Stephane Mallat, "A Wavelet Tour of signal Processing", Elsevier, 2006.
  18. D L Donoho and M. Jhonstone, "Wavelet shrinkage: Asymptopia? ", J. Roy. Stat. Soc. , SerB, Vol. 57, pp. 301-369, 1995.
  19. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
  20. IvanW. Selesnick, Richard G. Baraniuk, Nick G. Kingsbury, "The Dual Tree Complex Wavelet Transform", IEEE Signal Processing Magazine, November 2005.
  21. I. W. Selesnick, "The design of Hilbert transform pairs of wavelet bases via the flat delay filter," in Proc. IEEE Int. Conf. Acoust. , Speech, Signal Process. , May 2001.
  22. N. G. Kingsbury, "The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters," in Proc. Eighth IEEE DSP Workshop, Salt Lake City, UT, Aug. 9-12, 1998.
  23. I. W. Selesnick, "The double density dual tree DWT", IEEE Transactions on Signal Processing, Vol 52. No. 5, May 2004. 31
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Wavelet Transform Dual tree complex wavelet transform Double density wavelet transform Wavelet shrinkage Variance Stabilization. ifx