Characterization of Coronary Plaque by Using 2D Frequency Histogram of RF Signal

  • Satoshi Nakao
  • Kazuhiro Tokunaga
  • Noriaki Suetake
  • Eiji Uchino
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

Tissue characterization of plaque in coronary arteries by using histogram-based frequency spectrum in window is proposed. Radio frequency (RF) signals, observed by the intravascular ultrasound catheter rotating in the coronary artery, are used for the tissue characterization. The conventional methods only use the frequency spectrum at the point of tissue of concern. However, in the proposed method the 2D histogram, concerning frequency and spectral band intensity, created from the window matrix of RF signals, is employed. The accuracy of the tissue characterization has been improved compared with the conventional methods which only use the statistical information of the frequency spectrum.

Keywords

intravascular ultrasound radio frequency signal 2D histogram k-nearest neighbor 

Notes

Acknowledgments

Many thanks are due to Dr. T. Hiro for providing the IVUS data. Thanks are also due to Dr. G. Vachkov for his helpful discussions. This work was supported by the Grant-in-Aid for Scientific Research (B) of the Japan Society of Promotion of Science (JSPS) under the contract No.23300086.

References

  1. 1.
    Hodgson, J.M., Graham, S.P., Savakus, A.D., Dame, S.G., Stephens, D.N., Dhillon, P.S., Brands, D., Sheehan, H., Eberle, M.J.: Clinical percutaneous imaging of coronary anatomy using an over-the-wire ultrasound catheter system. Int. J. Card. Imaging 4, 187–193 (1989)CrossRefGoogle Scholar
  2. 2.
    Nair, A., Margolis, M.P., Kuban, B.D., Vince, D.G.: Automated coronary plaque characterisation with intravascular ultrasound backscatter: ex vivo validation. Euro. Intervention 3, 113–120 (2007)Google Scholar
  3. 3.
    Sathyanarayana, S., Carlier, S., Li, W., Thomas, L.: Characterisation of atherosclerotic plaque by spectral similarity of radiofrequency intravascular ultrasound signals. Euro. Intervention 5, 133–139 (2009)Google Scholar
  4. 4.
    Kubota, R., Uchino, E., Suetake, N.: Hierarchical k-nearest neighbor classification using feature and observation space information. IEICE Electron. Exp. 5, 114–119 (2008)CrossRefGoogle Scholar
  5. 5.
    Uchino, E., Suetake, N., Kubota, R., Koga, T., Hashimoto, G., Hiro, T.: An roc performance validation of hierarchical k-nearest neighbor classifier applied to tissue characterization using ivus-rf signal. In: International Workshop on Nonlinear Circuits and Signal Processing, pp. 333–336 (2009)Google Scholar
  6. 6.
    Dasarathy, B.: Nearest neighbor (NN) norms: nn pattern classification techniques. IEEE Computer Society Press tutorial. IEEE Computer Society Press, Washington (1991)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Satoshi Nakao
    • 1
  • Kazuhiro Tokunaga
    • 2
  • Noriaki Suetake
    • 1
  • Eiji Uchino
    • 1
    • 2
  1. 1.Yamaguchi UniversityYamaguchiJapan
  2. 2.Fuzzy Logic Systems InstituteIizukaJapan

Personalised recommendations