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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

S+SSPR 2018: Structural, Syntactic, and Statistical Pattern Recognition pp 376–385Cite as

Weighted Local Mutual Information for 2D-3D Registration in Vascular Interventions

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11004))

Abstract

In this paper, a new similarity measure, WLMI (Weighted Local Mutual Information), based on weighted patch and mutual information is proposed to register the preoperative 3D CT model to the intra-operative 2D X-ray images in vascular interventions. We embed this metric into the 2D-3D registration framework, where we show that the robustness and accuracy of the registration can be effectively improved by adapting the strategy of local image patch selection and the weighted joint distribution calculation based on gradient. Experiments on both synthetic and real X-ray image registration show that the proposed method produces considerably better registration results in a shorter time compared with the conventional MI and Normalized MI methods.

Thanks the support by Key projects of NSFC with Grant no. 61533016.

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Author information

Authors and Affiliations

  1. School of Astronautics, Beihang University, Beijing, 100191, China

    Cai Meng, Qi Wang & Yi Xie

  2. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China

    Cai Meng

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Shaoya Guan

Authors
  1. Cai Meng
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  2. Qi Wang
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  3. Shaoya Guan
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  4. Yi Xie
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Corresponding author

Correspondence to Cai Meng .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Xiao Bai

  2. University of York, York, United Kingdom

    Edwin R. Hancock

  3. IBM Research – Thomas J. Watson Research, Yorktown Heights, New York, USA

    Tin Kam Ho

  4. University of York, Heslington, York, United Kingdom

    Richard C. Wilson

  5. University of Cagliari, Cagliari, Italy

    Battista Biggio

  6. Data 61 - CSIRO, Canberra, Aust Capital Terr, Australia

    Antonio Robles-Kelly

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Meng, C., Wang, Q., Guan, S., Xie, Y. (2018). Weighted Local Mutual Information for 2D-3D Registration in Vascular Interventions. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_36

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  • DOI: https://doi.org/10.1007/978-3-319-97785-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97784-3

  • Online ISBN: 978-3-319-97785-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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