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JACIII Vol.21 No.1 pp. 67-73
doi: 10.20965/jaciii.2017.p0067
(2017)

Paper:

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IVUS Tissue Characterization of Coronary Plaque by Classification Restricted Boltzmann Machine

Nguyen Trong Kuong*, Eiji Uchino*,**, and Noriaki Suetake*

*Graduate School of Science and Engineering, Yamaguchi University
1677-1 Yoshida, Yamaguchi 753-8512, Japan

**Fuzzy Logic Systems Institute
680-41 Kawazu, Iizuka, Fukuoka 820-0067, Japan

Received:
March 8, 2016
Accepted:
November 7, 2016
Published:
January 20, 2017
Creative Commons License
Keywords:
atherosclerotic coronary plaque, radio frequency signal, intravascular ultrasound, classification restricted Boltzmann machine
Abstract
The tissue characterization of coronary plaque is an important task to assess the atherosclerotic process and the potential risks of their ruptures on patient. Thanks to intravascular ultrasound (IVUS) medical imaging technique, the reflected ultrasound signals from tissues are acquired, then be used to visualize inside the artery by the computer-assisted equipment. Often, the characterization of tissues is based on the analysis of their responding echo intensity. However, the domination of various factors and the data robustness are the realistic challenges of IVUS classification problems. The quality of the visualization totally depends on the proposed classifier of descriptive features along with its algorithm. In this study, our objective is to characterize IVUS tissues by using classification restricted Boltzmann machine (ClassRBM). We propose to binarize feature patterns extracted from time domain signals for the input of ClassRBM. The results show a better evaluation compared to the conventional integrated backscatter IVUS method (IB-IVUS) for the same task.
Cite this article as:
N. Kuong, E. Uchino, and N. Suetake, “IVUS Tissue Characterization of Coronary Plaque by Classification Restricted Boltzmann Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 67-73, 2017.
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References
  1. [1] A. C. v. d. Wal and A. E. Becker, “Atherosclerotic plaque rupture pathologic basis of plaque stability and instability,” Cardiovascular Research, Vol.41, No.2, pp. 334-344, 1999.
  2. [2] S. K. Mehta, J. R. McCrary, A. D. Frutkin, W. J. Dolla, and S. P. Marso, “Intravascular ultrasound radiofrequency analysis of coronary atherosclerosis: an emerging technology for the assessment of vulnerable plaque,” European Heart J., Vol.28, No.11, pp. 1283-1288, 2007.
  3. [3] M. Kawasaki, H. Takatsu, T. Noda, K. Sano, Y. Ito, K. Hayakawa, K. Tsuchiya, M. Arai, K. Nishigaki, G. Takemura, S. Minatoguchi, T. Fujiwara, and H. Fujiwara, “Invivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings,” Circulation, Vol.105, No.21, pp. 2487-2492, 2002.
  4. [4] A. Katouzian, B. Baseri, E. E. Konofagou, and S. G. Carlier, “Challenges in atherosclerotic plaque characterization with intravascular ultrasound (IVUS): From data collection to classification,” IEEE Trans. on Information Technology in Biomedicine, Vol.12, No.3, pp. 315-327, 2008.
  5. [5] F. Ciompi, C. Gatta, O. Pujol, O. Rodriguez-Leor, J. M. Ferré, and P. Radeva, “Reconstruction and analysis of intravascular ultrasound sequences,” Recent Advances in Biomedical Signal Processing, Vol.223, No.243, pp. 231-250, 2011.
  6. [6] S. E. Nissen and P. Yock, “Intravascular ultrasound: novel pathophysiological insights and current clinical applications,” Circulation, Vol.103, No.4, pp. 604-616, 2001.
  7. [7] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Trans. on Knowledge and Data Engineering, Vol.21, No.9, pp. 1263-1284, 2009.
  8. [8] A. D. Addabbo and R. Maglietta, “Parallel selective sampling method for imbalanced and large data classification,” Pattern Recognition Letters, Vol.62, pp. 61-67, 2015.
  9. [9] G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural Computation, Vol.14, No.8, pp. 1771-1800, 2002.
  10. [10] A. Fischer and C. Igel, “Training restricted Boltzmann machines: An introduction,” Pattern Recognition, Vol.47, No.1, pp. 25-39, 2014.
  11. [11] G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, Vol.18, No.7, pp. 1527-1544, 2006.
  12. [12] H. Larochelle, M. Mandel, R. Pascanu, and Y. Bengio, “Learning algorithms for the classification restricted Boltzmann machine,” Machine Learning Research, Vol.13, No.1, pp. 643-669, 2012.
  13. [13] G. E. Hinton, “A practical guide to training restricted Boltzmann machines,” Lecture Notes in Computer Science, Springer Berlin, Vol.7700, pp. 599-619, 2012.
  14. [14] M. Welling and G. E. Hinton, “A new learning algorithm for mean field Boltzmann machines,” Artificial Neural Networks – ICANN, Springer Berlin, Vol.2415, pp. 351-357, 2002.
  15. [15] M. D. Martino, A. Fernández, P. Iturralde, and F. Lecumberry, “Novel classifier scheme for imbalanced problems,” Pattern Recognition Letters, Vol.34, No.10, pp. 1146-1151, 2013.
  16. [16] V. López, A. Fernández, S. García, V. Palade, and F. Herrera, “An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics,” Information Sciences, Vol.250, pp. 113-141, 2013.
  17. [17] S. Wang and X. Yao, “Multiclass imbalance problems: analysis and potential solutions,” IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol.42, No.4, pp. 1119-1130, 2012.
  18. [18] T. Hastie and R. Tibshirani, “Classification by pairwise coupling,” Proc. of the 1997 Conf. on Advances in Neural Information Processing Systems 10, Cambridge, MA, USA, pp. 507-513, 1998.
  19. [19] R. Rifkin and A. Klautau, “In defense of one-vs-all classification,” J. of Machine Learning Research, Vol.5, pp. 101-141, 2004.
  20. [20] M. Kubat, R. Holte, and S. Matwin, “Learning when negative examples abound,” Proc. of the 9th European Conf. on Machine Learning (ECML ’97), Springer-Verlag, pp. 146-153, 1997.
  21. [21] D. Micari, M. Pascotto, A. R. Jayaweera, J. Sklenar, N. C. Goodman, and S. Kaul, “Cyclic variation in ultrasonic myocardial Integrated backscatter is due to phasic changes in the number of patent myocardial microvessels,” J. of Ultrasound in Medicine, Vol.25, No.8, pp. 1009-1019, 2006.

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