Bayesian and grAphical Models for Biomedical Imaging

First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers

  • M. Jorge Cardoso
  • Ivor Simpson
  • Tal Arbel
  • Doina Precup
  • Annemie Ribbens

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8677)

Table of contents

  1. Front Matter
  2. Christian Thode Larsen, J. Eugenio Iglesias, Koen Van Leemput
    Pages 1-12
  3. Chuyang Ye, Aaron Carass, Emi Murano, Maureen Stone, Jerry L. Prince
    Pages 13-24
  4. Florian Jug, Tobias Pietzsch, Dagmar Kainmüller, Jan Funke, Matthias Kaiser, Erik van Nimwegen et al.
    Pages 25-36
  5. Aina Frau-Pascual, Thomas Vincent, Jennifer Sloboda, Philippe Ciuciu, Florence Forbes
    Pages 37-48
  6. Stefano Pedemonte, Ciprian Catana, Koen Van Leemput
    Pages 61-72
  7. Monica Enescu, Mattias P. Heinrich, Esme Hill, Ricky Sharma, Michael A. Chappell, Julia A. Schnabel
    Pages 73-84
  8. Neil P. Oxtoby, Alexandra L. Young, Nick C. Fox, The Alzheimer’s Disease Neuroimaging Initiative, Pankaj Daga, David M. Cash et al.
    Pages 85-94
  9. Maxime Taquet, Jurriaan M. Peters, Simon K. Warfield
    Pages 95-106
  10. Kayhan N. Batmanghelich, Michael Cho, Raul San Jose, Polina Golland
    Pages 107-117
  11. Colm Elliott, Douglas L. Arnold, D. Louis Collins, Tal Arbel
    Pages 118-129
  12. Back Matter

About these proceedings


This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014.
The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.


bayesian modeling biomedical images classification computer vision functional modeling graphical modeling image analysis image segmentation inference algorithms machine learning multi-modal modeling neuro imaging probabilistic models reconstruction registration segmentation structural modeling

Editors and affiliations

  • M. Jorge Cardoso
    • 1
  • Ivor Simpson
    • 1
  • Tal Arbel
    • 2
  • Doina Precup
    • 2
  • Annemie Ribbens
    • 3
  1. 1.Centre for Medical ImagingUniversity College LondonLondonUK
  2. 2.Centre for Intelligent MachinesMcGill UniversityMontrealCanada
  3. 3.Medical Imaging Research CenterKatholieke Universiteit LeuvenLeuvenBelgium

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-12288-5
  • Online ISBN 978-3-319-12289-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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