Journal of Real-Time Image Processing

, Volume 13, Issue 1, pp 147–160 | Cite as

Configurable real-time motion estimation for medical imaging: application to X-ray and ultrasound

  • Nikolai AbramovEmail author
  • Maxim Fradkin
  • Laurence Rouet
  • Hans-Aloys Wischmann
Special Issue Paper


Motion estimation is a key building block of image processing pipelines in many different contexts, ranging from efficient coding of video sequences in the consumer electronics domain (TV, DVD, BD) to professional medical applications. Many block-matching approaches have been proposed in the literature for motion detection and compensation in general, including both lossless and lossy algorithms. However, in real-time medical imaging applications, characterized by high frame rates, the needs for low latency and jitter, accuracy and robustness against noise are quite difficult to achieve with standard block-matching methods. We introduce a new hybrid image processing approach to block-matching that takes advantage of both types of algorithms (lossless and lossy), adapts to the image content and noise, and provides high flexibility for the speed/accuracy tradeoff. The presented approach has been successfully tested on interventional X-ray fluoroscopy and cardiac ultrasound images sequences.


Motion estimation Medical imaging Block matching Configurable speedup Content-based classification 



The authors would like to gratefully acknowledge the continuous support from the “Image Processing” group at the St. Petersburg State Polytechnic University and the supervision of this work by Assistant Professor M. Bolsunovskaya—as well as the support, hosting, and collaboration by the Philips Research Medisys group in Suresnes, headed by Nicolas Villain.


  1. 1.
    Kim, J.N.: Fast full search motion estimation algorithm using early detection of impossible candidate vectors. IEEE Trans. Signal Process. 50(9), 2355–2365 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kuhn, P.: Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation. Kluwer Academic, Amsterdam (1999)CrossRefzbMATHGoogle Scholar
  3. 3.
    Jin, S., Lee, H., Jeong, J.: Hadamard transform based fast partial distortion elimination algorithm for lossless and lossy motion estimation. In: Congress of Image and Signal Process (2008)Google Scholar
  4. 4.
    Choi, C., Jeong, J.: New sorting-based partial distortion elimination algorithm for fast optimal motion estimation. IEEE Trans. Consum. Electron. 55(4), 2335–2340 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gao, X.Q., Duanmu, C.J., Zou, C.R.: A multilevel successive elimination algorithm for block matching motion estimation. IEEE Trans. Image Process. 9, 501–504 (2000)CrossRefGoogle Scholar
  6. 6.
    Cai, J.I., Pan, W.D.: Fast exhaustive-search motion estimation based on accelerated multilevel successive elimination algorithm with multiple passes. In: ICASSP, Dallas (2010)Google Scholar
  7. 7.
    Chen, W.G., Ling, Y.: Noise variance adaptive successive elimination algorithm for block motion estimation: application for video surveillance. IET Signal Process. 1(3), 150–155 (2004)CrossRefGoogle Scholar
  8. 8.
    Li, R., Zeng, B., Liou, M.L.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 4(4), 438–442 (1994)CrossRefGoogle Scholar
  9. 9.
    Po, L.M., Ma, W.C.: A novel four-step algorithm for fast block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 6, 313–317 (1996)CrossRefGoogle Scholar
  10. 10.
    Zhu, S., Ma, K.K.: A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. Image Process. 9, 287–290 (2000)CrossRefGoogle Scholar
  11. 11.
    Jain, J., Jain, A.: Displacement measurement and its application in interframe image coding. IEEE Trans. Commun. 29(12), 1799–1808 (1981)CrossRefGoogle Scholar
  12. 12.
    Bhaskaran, V., Konstantinides, K.: Image and video compression standards: algorithms and architectures, 2nd edn. Kluwer Academic, Amsterdam (1997)CrossRefGoogle Scholar
  13. 13.
    Sarwer, M.G., Wu, Q.M.J.: Efficient two step edge based partial distortion search for fast block motion estimation. IEEE Trans. Consum. Electron. 55, 2154–2162 (2009)CrossRefGoogle Scholar
  14. 14.
    Nie, Y., Ma, K.K.: Adaptive rood pattern search for fast block-matching motion estimation. IEEE Trans. Image Process. 11(12), 1442–1449 (2002)CrossRefGoogle Scholar
  15. 15.
    Pan, W.H., Chiang, C.K., Lai, S.H.: Adaptive Multi-reference downhill simplex search based on spatial-temporal motion smoothness criterion. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu (2007)Google Scholar
  16. 16.
    Gonzales, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  17. 17.
    Harris, C., Stephens, M.: A combines corner and edge detector. In: Proceedings of the 4th Alvey Vision ConferenceGoogle Scholar
  18. 18.
    Wang, H., Brady, M.: Real-time corner detection algorithm for motion estimation. Image Vis. Comput. 13(9), 695–703 (1995)CrossRefGoogle Scholar
  19. 19.
    Jung, S.M., Shin, S.C., Baik, H., Park, M.S.: New Fast successive elimination algorithm. In: Proceedings of 43rd IEEE Midwest Symposium on Circuits and Systems, Lansing (2000)Google Scholar
  20. 20.
    Cai, J., Pan, D.W.: On fast and accurate block-based motion estimation algorithms using particle swarm optimization. Inf. Sci. 197, 53–64 (2012)CrossRefGoogle Scholar
  21. 21.
    Saha, A., Mukherjee, J., Sural, S.: A neighborhood elimination approach for block matching in motion estimation. Signal Process. Image Commun. 26(8–9), 438–454 (2011)CrossRefGoogle Scholar
  22. 22.
    Zhang, P., Wei, P., Yu, H.-Y., Fei, C.: A novel search algorithm based on particle swarm optimization and simplex method for block motion estimation. Int. J. Digital Content Technol. Appl. 5(1), 76–86 (2011)CrossRefGoogle Scholar
  23. 23.
    Cuevas, E., Zaldivar, D., Perez-Cisneros, M., Sossa, H., Osuna, V.: Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Appl. Soft Comput. J. 13(6), 3047–3059 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Nikolai Abramov
    • 1
    Email author
  • Maxim Fradkin
    • 2
  • Laurence Rouet
    • 2
  • Hans-Aloys Wischmann
    • 3
  1. 1.Saint-Petersburg State Polytechnical UniversitySaint-PetersburgRussia
  2. 2.Philips Research MedisysParisFrance
  3. 3.Philips GmbH UB HealthcareHamburgGermany

Personalised recommendations