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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 419826, 8 pages
http://dx.doi.org/10.1155/2015/419826
Research Article

Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier

1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received 19 September 2014; Accepted 12 February 2015

Academic Editor: Yi Gao

Copyright © 2015 Jianming Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. J. Buss, D. Mereles, M. Emami et al., “Rapid assessment of longitudinal systolic left ventricular function using speckle tracking of the mitral annulus,” Clinical Research in Cardiology, vol. 101, no. 4, pp. 273–280, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. N. P. Nikitin, P. H. Loh, R. de Silva et al., “Prognostic value of systolic mitral annular velocity measured with Doppler tissue imaging in patients with chronic heart failure caused by left ventricular systolic dysfunction,” Heart, vol. 92, no. 6, pp. 775–779, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Matos, I. Kronzon, G. Panagopoulos, and G. Perk, “Mitral annular plane systolic excursion as a surrogate for left ventricular ejection fraction,” Journal of the American Society of Echocardiography, vol. 25, no. 9, pp. 969–974, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Tsang, H. Ahmad, A. R. Patel et al., “Rapid estimation of left ventricular function using echocardiographic speckle-tracking of mitral annular displacement,” Journal of the American Society of Echocardiography, vol. 23, no. 5, pp. 511–515, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. S. T. Nevo, M. van Stralen, A. M. Vossepoel et al., “Automated tracking of the mitral valve annulus motion in apical echocardiographic images using multidimensional dynamic programming,” Ultrasound in Medicine and Biology, vol. 33, no. 9, pp. 1389–1399, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Takemoto, T. Hozumi, K. Sugioka et al., “Automated three-dimensional analysis of mitral annular dynamics in patients with myocardial infarction using automated mitral annular tracking method,” Echocardiography, vol. 23, no. 8, pp. 658–665, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Veronesi, C. Corsi, E. G. Caiani et al., “Semi-automatic tracking for mitral annulus dynamic analysis using real-time 3D echocardiography,” in Proceedings of the Computers in Cardiology (CIC '06), vol. 33, pp. 113–116, September 2006. View at Scopus
  8. R. J. Schneider, D. P. Perrin, N. V. Vasilyev, G. R. Marx, P. J. Del Nido, and R. D. Howe, “Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow,” Medical Image Analysis, vol. 16, no. 2, pp. 497–504, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. R. J. Schneider, D. P. Perrin, N. V. Vasilyev, G. R. Marx, P. J. del Nido, and R. D. Howe, “Mitral annulus segmentation from 3D ultrasound using graph cuts,” IEEE Transactions on Medical Imaging, vol. 29, no. 9, pp. 1676–1687, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Maji, A. C. Berg, and J. Malik, “Efficient classification for additive kernel SVMs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 66–77, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Maji, A. C. Berg, and J. Maliks, “Classification using intersection kernel support vector machines is efficient,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. D.-C. He and L. Wang, “Texture features based on texture spectrum,” Pattern Recognition, vol. 24, no. 5, pp. 391–399, 1991. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Heikkilä, M. Pietikäinen, and J. Heikkilä, “A texture-based method for detecting moving objects,” in Proceedings of the 15th British Machine Vision Conference (BMVC '04), pp. 1–10, 2004.
  14. B. P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, and G. McGregor, “Boosted decision trees as an alternative to artificial neural networks for particle identification,” Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 543, no. 2-3, pp. 577–584, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. M. A. Kumar and M. Gopal, “A hybrid SVM based decision tree,” Pattern Recognition, vol. 43, no. 12, pp. 3977–3987, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Tu and X. Bai, “Auto-context and its application to high-level vision tasks and 3D brain image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1744–1757, 2010. View at Publisher · View at Google Scholar · View at Scopus