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International Symposium on Visual Computing

ISVC 2014: Advances in Visual Computing pp 11–20Cite as

Rigid Multimodal/Multispectral Image Registration Based on the Expectation-Maximization Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

Abstract

In this paper, we present a novel methodology for multimodal/multispectral rigid image registration. The proposed approach is formulated by using the Expectation-Maximization (EM) technique in order to estimate a geometric transformation that aligns the multiparametric images to register. In this approach, the images alignment relies on hidden stochastic random variables which allow to compare the multiparametric intensity values between images of different modalities. The methodology is basically composed by an EM-technique approach, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the rigid transformation are computed. The proposed algorithm was tested with different kinds of medical images; our results show that the proposed methodology is a good alternative for rigid multimodal/multispectral registration.

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Author information

Authors and Affiliations

  1. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Salvador Nava Mtz. S/N, Zona Universitaria, 78290, San Luis Potosí, SLP, México

    Edgar Arce-Santana, Daniel U. Campos-Delgado, Isnardo Reducindo & Aldo R. Mejía-Rodríguez

  2. Institute of Molecular Bioimaging and Physiology (IBFM), Italian National Research Council (CNR), Milan, Italy

    Giovanna Rizzo

Authors
  1. Edgar Arce-Santana
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  2. Daniel U. Campos-Delgado
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  3. Isnardo Reducindo
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  4. Aldo R. Mejía-Rodríguez
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  5. Giovanna Rizzo
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Arce-Santana, E., Campos-Delgado, D.U., Reducindo, I., Mejía-Rodríguez, A.R., Rizzo, G. (2014). Rigid Multimodal/Multispectral Image Registration Based on the Expectation-Maximization Algorithm. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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