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JACIII Vol.17 No.1 pp. 74-82
doi: 10.20965/jaciii.2013.p0074
(2013)

Paper:

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YURAGI Synthesis for Ultrasonic Human Brain Imaging

Naomi Yagi*1, Yoshitetsu Oshiro*2, Tomomoto Ishikawa*2,
and Yutaka Hata*3,*4

*1Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan

*2Ishikawa Hospital, 2-150 Bessho, Bessho-cho, Himeji, Hyogo 671-0221, Japan

*3Himeji Initiative in Computational Medical and Health Technology, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan

*4WPI Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan

Received:
April 20, 2012
Accepted:
December 22, 2012
Published:
January 20, 2013
Creative Commons License
Keywords:
YURAGI, ultrasonic system, human brain, fuzzy inference, data synthesis
Abstract
This paper proposes YURAGI synthesis for brain imaging under the skull. The advantage of the proposed method over conventional methods is that, using YURAGI synthesis, it is possible to obtain the effective results without image registration. Image registration is generally needed when more than two images are to be synthesized into one image. YURAGI synthesis does not need image registration; thus, its method is simpler than other methods that need image synthesis. The effectiveness of the proposed method was confirmed by comparing its error rate and accuracy with those of other methods. YURAGI leads the simple and energy-saving system with performing autoregulation. Autoregulation is utilized in many biological systems. In this study, YURAGI was applied to an ultrasound-based diagnostic medical imaging technique. The experimental results using YURAGI were superior to those using othermethods. Thus, YURAGI is useful for visualizing the human brain.1
1. This paper has been reviewed and accepted as a regular paper. The paper was invited and incorporated into the Special Issue on Advances in Fuzzy Inference and its Related Techniques.
Cite this article as:
N. Yagi, Y. Oshiro, T. Ishikawa, and Y. Hata, “YURAGI Synthesis for Ultrasonic Human Brain Imaging,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.1, pp. 74-82, 2013.
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References
  1. [1] K. A. Wear, “Autocorrelation and Cepstral Methods for Measurement of Tibial Cortical Thickness,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, Vol.50, No.5, pp. 655-660, June 2003.
  2. [2] J. Krautkramer and H. Krautkramer (Ed.), “Ultrasonic Testing of Materials,” Springer-Verlag, Berlin 1990.
  3. [3] R. N. Thomas et al., “Three-Dimensional Ultrasound,” Lippincott Williams and Wilkins, 1999.
  4. [4] F. Vignon, J.-F. Aubry, M. Tanter, A. Margoum, and M. Fink, “Dual-Arrays Brain Imaging Prototype: Experimental In Vitro Results,” IEEE Int. Ultrasonics Symposium, pp. 504-507, 2005.
  5. [5] J. White, G. T. Clement, and K. Hynynen, “Transcranial Ultrasound Focus Reconstruction with Phase and Amplitude Correction,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency control, pp. 1518-1522, Vol.52, No.9, 2005.
  6. [6] P. Leissner, L.-E. Lindholm, and I Petersén, “Alpha amplitude dependence on skull thickness as measured by ultrasound technique,” Electroencephalography and Clinical Neurophysiology, Vol.29, Issue 4, pp. 392-399, 1970.
  7. [7] Y. Hata, S. Kobashi, K. Kondo, Y. T. Kitamura, and T. Yanagida, “Transcranial Ultrasonography System for Visualizing Skull and Brain Surface Aided by Fuzzy Expert System,” IEEE Trans. on Systems, Man and Cybernetics, Vol.35, No.6, pp. 1360-1373, 2005.
  8. [8] M. Kimura, S. Kobashi, K. Kondo, Y. Hata, Y. T. Kitamura, and T. Yanagida, “Fuzzy Ultrasonic Imaging System for Visualizing Brain Surface under Skull Considering Ultrasonic Refraction,” in Proc. 2006 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 3790-3794, 2007.
  9. [9] G. Hiramatsu, Y. Ikeda, S. Imawaki, Y. T. Kitamura, T. Yanagida, S. Kobashi, and Y. Hata, “Trans-skull Imaging System by Ultrasonic Array Probe,” Proc. of 2009 IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1116-1121, 2009.
  10. [10] M. D. McDonnell and D. Abbott, “What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology,” PLoS Computational Biology, Vol.5, e1000348, 2009.
  11. [11] T. Mori and S. Kai, “Noise-induced entrainment and stochastic resonance in human brain waves,” Phy. Rev. Letters, Vol.88, 218101, 2002.
  12. [12] P. Hänggi, “Stochastic resonance in biology – How noise can enhance detection of weak signals and help improve biological information processing,” ChemPhysChem, Vol.3, pp. 285-290, 2002.
  13. [13] L. Ke, X. Jianping, K. Dongmei, and Z. Na, “A Method of Evaluating the Signal to Noise Ratio Based on Duffing Time Series,” Proc. of the 2009 Int. Conf. on Measuring Technology and Mechatronics Automation, Vol.1, pp. 399-402, 2009.
  14. [14] Y. Hotta, T. Kanki, N. Asakawa, H. Tabata, and T. Kawai, “Cooperative Dynamics of an Artificial Stochastic Resonant System,” Appl. Phys. Express, Vol.1, 2008.
  15. [15] K. Wiesenfeld and F. Jaramillo, “Minireview of stochastic resonance,” Chaos, Vol.8, pp. 539-548, 1998.
  16. [16] L. A. Zadeh and J. Kacprzyk, “Fuzzy Logic for the Management of Uncertainly,” New York: Wiley, 1992.
  17. [17] S. G. Nurzaman, Y. Matsumoto, Y. Nakamura, S. Koizumi, and H. Ishiguro, “Yuragi-based adaptive searching behavior in mobile robot: From bacterial chemotaxis to Levy walk,” IEEE Int. Conf. on Robotics and Biomimetics, pp. 806-811, 2009.
  18. [18] S. G. Nurzaman, Y. Matsumoto, Y. Nakamura, S. Koizumi, and H. Ishiguro, “Biologically inspired adaptive mobile robot search with and without gradient sensing,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 142-147, 2009.
  19. [19] G. Hiramatsu, S. Kobashi, Y. Hata, and S. Imawaki, “Ultrasonic Large Intestine Thickness Determination System for Low Anterior Resection,” in Proc. 2008 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 3072-3076, 2008.
  20. [20] J. Yasui, S. Kobashi, K. Kondo, and Y. Hata, “Fuzzy Ultrasonic Testing System with Columnar Rod,” Proc. of 2006 Int. Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2006), pp. 903-906, Dec. 2006.
  21. [21] N. Yagi, Y. Oshiro, O. Ishikawa, G. Hiramatsu, Y. Hata, Y .Kitamura, and T. Yanagida, “Data synthesis for trans-skull brain imaging by 0.5 and 1.0MHz ultrasonic array systems,” Proc. of 2010 IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1524-1529, 2010.
  22. [22] N. Yagi, Y. Oshiro, O. Ishikawa, Y. Hata, Y. T. Kitamura, and T. Yanagida, “YURAGI: analysis for trans-skull brain visualizing by ultrasonic array probe,” Proc. of SPIE Defence, Security and Sensing 2011, pp. 805813-1-9, 2011.

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