Detection of Individual Microbubbles Using Wavelet Transform Based on a Theoretical Bubble Oscillation Model

  • Yujin Zong
  • Bin Li
  • Mingxi Wan
  • Supin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Detecting individual microbubbles is important for the quantification of the amount of bubbles in the tissues, determination of microvascular volume and targeted microbubble imaging. We took the advantage of a theoretical bubble oscillation model to construct a matched wavelet, i.e. bubble wavelet as mother wavelet to detect individual microbubble using wavelet transform. The experimental echoes with different levels of added noises were processed. The results showed significant improvement even for an Echo-Noise-Ratio (ENR in ) of -20 dB and the spatial location demonstrated very close agreement with the original experimental echo. This technique was much better than those based on harmonic analysis especially under the circumstance of short pulse insonation.


Wavelet Transform Wavelet Coefficient Mother Wavelet Continuous Wavelet Transform Matched Filter 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yujin Zong
    • 1
  • Bin Li
    • 1
  • Mingxi Wan
    • 1
  • Supin Wang
    • 1
  1. 1.Department of Biomedical engineering, Key Laboratory of Biomedical Information Engineering of Ministry of EducationXi’an Jiaotong UniversityXi’anChina

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