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Genetic Algorithm and Fisher Discriminant Analysis based Wavelet Thresholding for Speckle Noise Filtering in Ultrasound Images

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Shisir Mia, Md. Mahfuz Reza, Mohammad Motiur Rahman

Shisir Mia, Md. Mahfuz Reza and Mohammad Motiur Rahman. Genetic Algorithm and Fisher Discriminant Analysis based Wavelet Thresholding for Speckle Noise Filtering in Ultrasound Images. International Journal of Computer Applications 174(30):13-18, April 2021. BibTeX

	author = {Shisir Mia and Md. Mahfuz Reza and Mohammad Motiur Rahman},
	title = {Genetic Algorithm and Fisher Discriminant Analysis based Wavelet Thresholding for Speckle Noise Filtering in Ultrasound Images},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2021},
	volume = {174},
	number = {30},
	month = {Apr},
	year = {2021},
	issn = {0975-8887},
	pages = {13-18},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2021921231},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Speckle noise is a significant property of medical ultrasound imaging, and it typically degrades the resolution and contrast of images, sinking the diagnostic importance of the imaging modality. As a consequence, filtering speckle noise in the ultrasound images is a critical step for further analysis by the medical experts. In this paper, a speckle noise filtering technique have been suggested via wavelet thresholding for denosing ultrasound images. For each wavelet coefficient, in the first step, two optimal threshold parameters are estimated through the genetic algorithm and fisher discriminant analysis respectively. In the second step, thresholding of wavelet coefficient is performed by both threshold parameters. Finally, thresholded coefficient which corresponds to lowest mean square error is selected for obtaining the denoised ultrasound image. Results show that, the proposed technique outperforms different existing denoising techniques.


  1. Achim, A., Bezerianos, A., and Tsakalides, P. 2001. Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE transactions on medical imaging, 20(8), 772-783.
  2. Donoho, D. L., and Johnstone, I. M. 1995. Adapting to unknown smoothness via wavelet shrinkage. Journal of the american statistical association, 90(432), 1200-1224.
  3. Donoho, D. L., and Johnstone, J. M. 1994. Ideal spatial adaptation by wavelet shrinkage. biometrika, 81(3), 425-455.
  4. Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., and Picard, D. 1995. Wavelet shrinkage: asymptopia?. Journal of the Royal Statistical Society: Series B (Methodological), 57(2), 301-337.
  5. Fodor, I. K., and Kamath, C. 2003. Denoising through wavelet shrinkage: an empirical study. Journal of Electronic Imaging, 12(1), 151-160.
  6. Chang, S. G., Yu, B., and Vetterli, M. 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE transactions on image processing, 9(9), 1532-1546.
  7. Chang, S. G., Yu, B., and Vetterli, M. 2000. Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Transactions on image Processing, 9(9), 1522-1531.
  8. Chang, S. G., Yu, B., and Vetterli, M. 2000. Wavelet thresholding for multiple noisy image copies. IEEE Transactions on Image Processing, 9(9), 1631-1635.
  9. Jansen, M. 2001. Noise Reduction by Wavelet Thresholding.-Springer Verlag, Lecture notes in Statistics. Vol. 161.
  10. Muhsen, Z. F., Dababneh, M., and Al-Nsour, A. 2011. Wavelet and optimal requantization methodology for lossy fingerprint compression. Int. Arab J. Inf. Technol., 8(4), 383-387.
  11. Rahman, M. M., PK, M. K., and Uddin, M. S. 2014. Optimum Threshold Parameter Estimation of Wavelet Coefficients Using Fisher Discriminant Analysis for Speckle Noise Reduction. International Arab Journal of Information Technology (IAJIT), 11(6).
  12. Mukhopadhyay, S., and Mandal, J. K. 2013. Wavelet based denoising of medical images using sub-band adaptive thresholding through genetic algorithm. Procedia technology, 10, 680-689.
  13. Goldberg, D. E. 1989. Genetic algorithms in search, optimization, and machine learning. Addison. Reading.
  14. Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
  15. Abo-Eleneen, Z. A. 2011. Thresholding based on Fisher linear discriminant. Journal of pattern recognition research, 2, 326-334.
  16. Sattar, F., Floreby, L., Salomonsson, G., and Lovstrom, B. 1997. Image enhancement based on a nonlinear multiscale method. IEEE transactions on image processing, 6(6), 888-895.
  17. Sivakumar, R., Gayathri, M. K., and Nedumaran, D. 2010. Speckle filtering of ultrasound b-scan images-a comparative study between spatial and diffusion filters. In 2010 IEEE Conference on Open Systems (ICOS 2010) (pp. 80-85). IEEE.


Genetic Algorithm; Fisher Discriminant Analysis; Speckle Noise; Ultrasound Image; Wavelet Transform