Advertisement

Wall Motion Classification of Stress Echocardiography Based on Combined Rest-and-Stress Data

  • Sarina Mansor
  • Nicholas P. Hughes
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

In this paper, we represent a new framework that performs automated local wall motion analysis based on the combined information derived from a rest and stress sequence (a full stress echocardiography study). Since cardiac data inherits time-varying and sequential properties, we introduce a Hidden Markov Model (HMM) approach to classify stress echocardiography. A wall segment model is developed for a normal and an abnormal heart and experiments are performed on rest, stress and rest-and-stress sequences. In an assessment using n=44 datasets, combined rest-and-stress analysis shows an improvement in classification (84.17%) over individual rest (73.33%) and stress (68.33%).

Keywords

Hide Markov Model Wall Motion Stress Echocardiography Wall Motion Analysis Cardiac Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Noble, J.A.: Cardiology meets Image Analysis: just another application or can image analysis usefully influence clinical practice? In: IEEE International Workshop on Computer Vision in Biomedical Image Analysis, Beijing, China (2005)Google Scholar
  2. 2.
    Mansor, S., Noble, J.A., Hughes, N.P.: Regional Left Ventricular Wall Motion Analysis using Hidden Markov Models. In: Proceedings of Medical Image Understanding and Analysis, Wales, United Kingdom, pp. 136–140 (2007)Google Scholar
  3. 3.
    Mansor, S., Noble, J.A.: Local Wall Motion Classification of Stress Echocardiography using Hidden Markov Models. In: IEEE International Symposium on Biomedical Imaging, ISBI 2008, Paris, France (2008)Google Scholar
  4. 4.
    Esther Leung, K.Y., Bosch, J.G.: Local Wall Motion Classification in Echocardiograms using Shape Models and Orthomax Rotations. In: Sachse, F.B., Seemann, G. (eds.) FIHM 2007. LNCS, vol. 4466, pp. 1–11. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Qazi, M., Fung, G., et al.: Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks. In: International Joint Conferences on Artificial Intelligence, IJCAI 2007, Hyderabad, India (2007)Google Scholar
  6. 6.
    Picano, E.: Stress Echocardiography, 4th edn. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Suinesiaputra, A., Frangi, A.F., et al.: Automatic Prediction of Myocardial Contractility Improvement in Stress MRI Using Shape Morphometrics with Independent Component Analysis. In: Christensen, G.E., Sonka, M. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 321–332. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Rabiner, L.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  9. 9.
    Lang, R.M., et al.: Recommendations for chamber quantification. European Journal Echocardiography 7, 79–108 (2006)CrossRefGoogle Scholar
  10. 10.
    He, Y., Kundu, A.: 2-D Shape Classification Using Hidden Markov Model. IEEE Trans. IEEE Trans. Pattern Analysis and Machine Intelligence 13(1), 1172–1184 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sarina Mansor
    • 1
  • Nicholas P. Hughes
    • 1
  • J. Alison Noble
    • 1
  1. 1.Biomedical Image Analysis (BioMedIA) Laboratory, Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordUnited Kingdom

Personalised recommendations