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

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Abstract

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.

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References

  1. AL Achim AN Bezerianos PA Tsakalides (2001) ArticleTitleNovel bayesian multiscale method for speckle removal in medical ultrasound images IEEE Trans On Medical Imaging 20 772–783 Occurrence Handle1:STN:280:DC%2BD3MvmslyhsQ%3D%3D

    CAS  Google Scholar 

  2. CH Chirungrueng AI Suvichakorn (2001) ArticleTitleFast edge processing noise reduction for ultrasound images IEEE Trans Nuclear Science 48 IssueID3 849–854

    Google Scholar 

  3. JG Abbott FL Thurstone (1979) ArticleTitleAcoustic speckle: theory and experimental analysis Ultrason. Imag 1 303–324 Occurrence Handle10.1016/0161-7346(79)90024-5 Occurrence Handle1:STN:280:Bi%2BB3sjivFY%3D

    Article  CAS  Google Scholar 

  4. JW Goodman (1976) ArticleTitleSome fundamental properties of speckle J. Opt. Soc. Am 66 1145–1150 Occurrence Handle10.1364/JOSA.66.001145

    Article  Google Scholar 

  5. AN Jain (1989) Fundamentals of Digital Image Processing Englewood Cliffs NJ, Prentice-Hall

    Google Scholar 

  6. P-C Li M-J Chen (2002) ArticleTitleStrain compounding, a new approach for speckle reduction IEEE Trans. Ultr. Ferroelectonic Freq. Cont. 49 39–46

    Google Scholar 

  7. OD Husbey TO Lie TH Longo (2001) ArticleTitleBayesian 2-D deconvolution, a model for diffuse ultrasound scattering IEEE Trans. Ultr. Ferroelectonic Freq. Cont 48 121–130

    Google Scholar 

  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. 172–175

  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, 1984

  10. ER Ritenour TR Nelson UO Raff (1998) ArticleTitleApplication of the median filter to digital radiographic images Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing . 2311–2314

    Google Scholar 

  11. TA Loupas WN Mcdicken PL Allan (1989) ArticleTitleAn adaptive weighted median filter for speckle suppression in medical ultrasonic images IEEE Trans. Circuits Syst. 36 129–135 Occurrence Handle10.1109/31.16577

    Article  Google Scholar 

  12. RN Czerwinsky DL Jones SuffixJr WD O’Brien (1995) ArticleTitleUltrasound speckle reduction by directional median filtering Proc. Int. Conf. Image Processing 1 358–361 Occurrence Handlefull_text||10.1109/ICIP.1995.529720

    Article  Google Scholar 

  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 302–306

  14. L Yin R Yang M Gabbou Y Neuvo (1996) ArticleTitleWeighted median filters: a tutorial IEEE Trans. Circuits Syst. II 43 157–192 Occurrence Handle10.1109/82.486465

    Article  Google Scholar 

  15. TS Huang GY Yang GY Tang (1979) ArticleTitleA fast two-dimensional median filtering algorithm IEEE Trans. Acoust. Speech, Signal Processing ASSP-27 13–18

    Google Scholar 

  16. I Pitas AN Venetsanopolous (1990) Nonlinear Digital Filter-Principles and Applications Kluwer Academic Norwell, MA

    Google Scholar 

  17. X Zong AF Laine EA Geiser (1998) ArticleTitleSpeckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing IEEE Trans. Med. Imag 17 532–540 Occurrence Handle1:STN:280:DyaK1M%2FlvFCnsA%3D%3D

    CAS  Google Scholar 

  18. CT Lin CF Juang (1997) ArticleTitleAn adaptive neural fuzzy filter and its applications IEEE Trans Systems, Man, and Cybernetics—Part b: Cybernetics 27 635–656 Occurrence Handle1:STN:280:DC%2BD1c%2FpvVGntQ%3D%3D

    CAS  Google Scholar 

  19. F Russo (1998) ArticleTitleA Evolutionary neural fuzzy system for data filtering IEEE Inst. Meas. Tech. Conf. St Paul Minnesola . 826–831

    Google Scholar 

  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, 2001

  21. X Hong C Harris PA Wilson (1999) ArticleTitleNeurofuzzy state identification using prefiltering IEE Proc. Cont. Theory Appl. 146 234–240 Occurrence Handle10.1049/ip-cta:19990121

    Article  Google Scholar 

  22. F Russo (1999) ArticleTitleEvolutionary neural fuzzy systems for noise cancellation in image data IEEE Trans Instr Meas 48 915–920

    Google Scholar 

  23. HS Wong L Guan (2001) ArticleTitleA neural learning approach for adaptive image restoration using a fuzzy model-based network architecture IEEE Trans Neural Networks 12 516–531 Occurrence Handle1:STN:280:DC%2BD1c%2FptFKgtg%3D%3D

    CAS  Google Scholar 

  24. SK Sinha F Karray (2002) ArticleTitleClassification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm IEEE Trans Neural Networks 13 393–401 Occurrence Handle10.1109/72.991425 Occurrence Handle1:STN:280:DC%2BD1c%2Fos1yqsw%3D%3D

    Article  CAS  Google Scholar 

  25. V Boskovitz H Guterman (2002) ArticleTitleAn adaptive neuro-fuzzy system for automam tic image segmentation and edge detection IEEE Trans Fuzzy Systems 10 247–262

    Google Scholar 

  26. M Russo (2000) ArticleTitleGenetic fuzzy learning IEEE Trans Evol Comput 4 259–273

    Google Scholar 

  27. EA Durant GH Wakefield (2002) ArticleTitleEfficient model fitting using a genetic algorithm: pole-zero approximations of HRTFs IEEE Trans Speech Audio Processing 10 18–27

    Google Scholar 

  28. Goldberg DE: Genetic algorithms. In: Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989

  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, 2003

  30. Rafiee A: Sonography images enhancement by neuro-fuzzy algorithms. Ph.D. Thesis, Research and Science campus, Islamic Azad University, Tehran, Iran, 2003

  31. Saifipour N: On-line genetic algorithm in optimization and control. Ph.D. Thesis Amirkabir University, Tehran, Iran, 2002

  32. J Canny (1986) ArticleTitleA computational approach to edge detector IEEE Trans. Pattern Anal. Machine Intel. Vol. PAMI-8 . 679–697 Occurrence Handle10.1109/TPAMI.1986.4767851

    Article  Google Scholar 

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Acknowledgments

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

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Authors and Affiliations

  1. Science and Research Campus, Islamic Azad University, Tehran, Iran

    Ali Rafiee PhD

  2. Amirkabir University, Tehran, Iran

    Mohammad Hasan Moradi Assistant Professor

  3. Shiraz Biomedical University, Shiraz, Iran

    Mohammad Reza Farzaneh Assistant Professor

  4. No. 11 Sarbaz Boulevard, Havabord, Shiraz, Iran

    Ali Rafiee PhD

Authors
  1. Ali Rafiee PhD
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  2. Mohammad Hasan Moradi Assistant Professor
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  3. Mohammad Reza Farzaneh Assistant Professor
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Correspondence to Ali Rafiee PhD.

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Rafiee, A., Moradi, M.H. & Farzaneh, M.R. Novel Genetic-Neuro-Fuzzy Filter for Speckle Reduction from Sonography Images . J Digit Imaging 17, 292–300 (2004). https://doi.org/10.1007/s10278-004-1026-2

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  • DOI: https://doi.org/10.1007/s10278-004-1026-2

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