Journal of Digital Imaging

, Volume 17, Issue 4, pp 292–300 | Cite as

Novel Genetic-Neuro-Fuzzy Filter for Speckle Reduction from Sonography Images

  • Ali Rafiee
  • Mohammad Hasan Moradi
  • Mohammad Reza Farzaneh


Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations.

The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filter’s output to the edge detection algorithm and observing its ability to detect edge pixels.

Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.


Ultrasound speckle real-time genetic algorithm neuro-fuzzy genetic-neuro-fuzzy 



The authors thank the anonymous reviewers of this paper for their detailed comments and suggestions.


  1. 1.
    Achim, AL, Bezerianos, AN, Tsakalides, PA 2001Novel bayesian multiscale method for speckle removal in medical ultrasound imagesIEEE Trans On Medical Imaging20772783Google Scholar
  2. 2.
    Chirungrueng, CH, Suvichakorn, AI 2001Fast edge processing noise reduction for ultrasound imagesIEEE Trans Nuclear Science48849854Google Scholar
  3. 3.
    Abbott, JG, Thurstone, FL 1979Acoustic speckle: theory and experimental analysisUltrason. Imag1303324CrossRefGoogle Scholar
  4. 4.
    Goodman, JW 1976Some fundamental properties of speckleJ. Opt. Soc. Am6611451150CrossRefGoogle Scholar
  5. 5.
    Jain, AN 1989Fundamentals of Digital Image Processing Englewood CliffsNJ, Prentice-HallGoogle Scholar
  6. 6.
    Li, P-C, Chen, M-J 2002Strain compounding, a new approach for speckle reductionIEEE Trans. Ultr. Ferroelectonic Freq. Cont.493946Google Scholar
  7. 7.
    Husbey, OD, Lie, TO, Longo, TH 2001Bayesian 2-D deconvolution, a model for diffuse ultrasound scatteringIEEE Trans. Ultr. Ferroelectonic Freq. Cont48121130Google Scholar
  8. 8.
    KHZ Abd-Elmoniem, Ya M Kadad, AB BA M Youssef: Real time ultrasound speckle reduction and coherent enhancement. In International Conference on Image Processing, 10–13 Sept. 2000: Image Processing Proceeding, 2000 IEEE, Vol 1, pp. 172–175Google Scholar
  9. 9.
    Ioannidis AL, Kazakos DA, Watson DD: Application of median filtering on nuclear medicine scintigram images. In: Proceedings of the 7th International Conference on Pattern Recognition, Montreal, Canada, pp. 33–36, 1984Google Scholar
  10. 10.
    Ritenour, ER, Nelson, TR, Raff, UO 1998Application of the median filter to digital radiographic imagesProc. IEEE Int. Conf. Acoust. Speech, Signal Processing.23112314Google Scholar
  11. 11.
    Loupas, TA, Mcdicken, WN, Allan, PL 1989An adaptive weighted median filter for speckle suppression in medical ultrasonic imagesIEEE Trans. Circuits Syst.36129135CrossRefGoogle Scholar
  12. 12.
    Czerwinsky, RN, Jones, DL,Jr, O’Brien, WD 1995Ultrasound speckle reduction by directional median filteringProc. Int. Conf. Image Processing1358361CrossRefGoogle Scholar
  13. 13.
    Hu, J, Hu, X (1994) “Application of median filter to speckle suppression in intravascular ultrasound images” Intelligent Information Systems 1994. In: Proc. 1994 2nd IEEE Australian and New Zealand Conf. Intelligent Information Systems, 29 Nov–2 Dec 1994, pp 302–306Google Scholar
  14. 14.
    Yin, L, Yang, R, Gabbou, M, Neuvo, Y 1996Weighted median filters: a tutorialIEEE Trans. Circuits Syst. II43157192CrossRefGoogle Scholar
  15. 15.
    Huang, TS, Yang, GY, Tang, GY 1979A fast two-dimensional median filtering algorithmIEEE Trans. Acoust. Speech, Signal ProcessingASSP-271318Google Scholar
  16. 16.
    Pitas, I, Venetsanopolous, AN 1990Nonlinear Digital Filter-Principles and ApplicationsKluwer AcademicNorwell, MAGoogle Scholar
  17. 17.
    Zong, X, Laine, AF, Geiser, EA 1998Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processingIEEE Trans. Med. Imag17532540Google Scholar
  18. 18.
    Lin, CT, Juang, CF 1997An adaptive neural fuzzy filter and its applicationsIEEE Trans Systems, Man, and Cybernetics—Part b: Cybernetics27635656Google Scholar
  19. 19.
    Russo, F 1998A Evolutionary neural fuzzy system for data filteringIEEE Inst. Meas. Tech. Conf. St Paul Minnesola.826831MAY 18–21Google Scholar
  20. 20.
    Cojbasic F, Nikolic VD: An approach to neuro-fuzzy filtering for communications and control. In: 5th IEEE International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service, TELSIKS 2001, Vol. 2, pp. 719–722, 2001Google Scholar
  21. 21.
    Hong, X, Harris, C, Wilson, PA 1999Neurofuzzy state identification using prefilteringIEE Proc. Cont. Theory Appl.146234240CrossRefGoogle Scholar
  22. 22.
    Russo, F 1999Evolutionary neural fuzzy systems for noise cancellation in image dataIEEE Trans Instr Meas48915920Google Scholar
  23. 23.
    Wong, HS, Guan, L 2001A neural learning approach for adaptive image restoration using a fuzzy model-based network architectureIEEE Trans Neural Networks12516531Google Scholar
  24. 24.
    Sinha, SK, Karray, F 2002Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithmIEEE Trans Neural Networks13393401CrossRefGoogle Scholar
  25. 25.
    Boskovitz, V, Guterman, H 2002An adaptive neuro-fuzzy system for automam tic image segmentation and edge detectionIEEE Trans Fuzzy Systems10247262Google Scholar
  26. 26.
    Russo, M 2000Genetic fuzzy learningIEEE Trans Evol Comput4259273Google Scholar
  27. 27.
    Durant, EA, Wakefield, GH 2002Efficient model fitting using a genetic algorithm: pole-zero approximations of HRTFsIEEE Trans Speech Audio Processing101827Google Scholar
  28. 28.
    Goldberg DE: Genetic algorithms. In: Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989Google Scholar
  29. 29.
    Rafiee Kerachi A, Moradi MH, Farzaneh MR: Impulse noise reduction in sonography images by using neuro-fuzzy filters. Second Iranian Conference on Machine Vision, Image Processing & Applications, MVIP2003, KhajehNasir Al-Din Toosi University Tehran, Iran, 2003Google Scholar
  30. 30.
    Rafiee A: Sonography images enhancement by neuro-fuzzy algorithms. Ph.D. Thesis, Research and Science campus, Islamic Azad University, Tehran, Iran, 2003Google Scholar
  31. 31.
    Saifipour N: On-line genetic algorithm in optimization and control. Ph.D. Thesis Amirkabir University, Tehran, Iran, 2002Google Scholar
  32. 32.
    Canny, J 1986A computational approach to edge detectorIEEE Trans. Pattern Anal. Machine Intel. Vol. PAMI-8.679697CrossRefGoogle Scholar

Copyright information

© SCAR (Society for Computer Applications in Radiology) 2004

Authors and Affiliations

  • Ali Rafiee
    • 1
    • 4
  • Mohammad Hasan Moradi
    • 2
  • Mohammad Reza Farzaneh
    • 3
  1. 1.Science and Research CampusIslamic Azad UniversityTehranIran
  2. 2.Amirkabir UniversityTehranIran
  3. 3.Shiraz Biomedical UniversityShirazIran
  4. 4.No. 11 Sarbaz BoulevardHavabordIran

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