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Automated Selection of Standardized Planes from Ultrasound Volume

  • Bahbibi Rahmatullah
  • Aris Papageorghiou
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

The search for the standardized planes in a 3D ultrasound volume is a hard and time consuming process even for expert physicians. A scheme for finding the standardized planes would be beneficial in advancing the use of volumetric ultrasound for clinical diagnosis. In this paper, we propose a new method to automatically select the standard plane from the fetal ultrasound volume for the application of fetal biometry measurement. To our knowledge, this is the first study in the fetal ultrasound domain. The method is based on the AdaBoost learning algorithm and has been evaluated on a set of 30 volumes. The experimental results are promising with a recall rate of 91.29%. We believe this will increase the accuracy and efficiency in patient monitoring and care management in obstetrics, specifically in detecting growth restricted fetuses.

Keywords

AdaBoost Ultrasound Standardized plane Detection 

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References

  1. 1.
    Lawn, J.E., Cousens, S., Zupan, J.: Million neonatal deaths: When? Where? Why? Lancet 365, 891–900 (2005)CrossRefGoogle Scholar
  2. 2.
    Barker, D.J.P.: Adult consequences of fetal growth restriction. Clinical Obstetrics and Gynecology 49, 270–283 (2006)CrossRefGoogle Scholar
  3. 3.
    Chan, L.W., Fung, T.Y., Leung, T.Y., Sahota, D.S., Lau, T.K.: Volumetric (3D) imaging reduces inter- and intraobserver variation of fetal biometry measurements. Ultrasound in Obstetrics and Gynecology 33, 447–452 (2009)CrossRefGoogle Scholar
  4. 4.
    Elliott, S.T.: Volume ultrasound: The next big thing? British Journal of Radiology 81, 8–9 (2008)CrossRefGoogle Scholar
  5. 5.
    Leung, K.Y.E., et al.: Sparse registration for three-dimensional stress echocardiography. IEEE Transactions on Medical Imaging 27, 1568–1579 (2008)CrossRefGoogle Scholar
  6. 6.
    Carneiro, G., Georgescu, B., Good, S., Comaniciu, D.: Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Transactions on Medical Imaging 27, 1342–1355 (2008)CrossRefGoogle Scholar
  7. 7.
    Hadlock, F.P., Deter, R.L., Harrist, R.B., Park, S.K.: Fetal abdominal circumference as a predictor of menstrual age. American Journal of Roentgenology 139, 367–370 (1982)CrossRefGoogle Scholar
  8. 8.
    Campbell, S., Wilkin, D.: Ultrasonic measurement of fetal abdomen circumference in the estimation of fetal weight. British Journal of Obstetrics and Gynaecology 82, 689–697 (1975)CrossRefGoogle Scholar
  9. 9.
    Chitty, L.S., Altman, D.G., Henderson, A., Campbell, S.: Charts of fetal size: 3. Abdominal measurements. British Journal of Obstetrics and Gynaecology 101, 125–131 (1994)CrossRefGoogle Scholar
  10. 10.
    Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates, pp. 193–199 (1997)Google Scholar
  11. 11.
    Karavides, T., Leung, K.Y.E., Paclik, P., Hendriks, E.A., Bosch, J.G.: Database guided detection of anatomical landmark points in 3D images of the heart, pp. 1089–1092. Rotterdam (2010)Google Scholar
  12. 12.
    Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  13. 13.
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55, 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Ochs, R.A., et al.: Automated classification of lung bronchovascular anatomy in CT using AdaBoost. Medical Image Analysis 11, 315–324 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bahbibi Rahmatullah
    • 1
  • Aris Papageorghiou
    • 2
  • J. Alison Noble
    • 1
  1. 1.Institute of Biomedical Engineering, Dept. of Engineering ScienceUniversity of OxfordUK
  2. 2.Nuffield Dept of Obstetrics and Gynaecology, John Radcliffe HospitalUniversity of OxfordUK

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