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International Workshop on Graphs in Biomedical Image Analysis

International Workshop on Imaging Genetics

International Workshop on Mathematical Foundations of Computational Anatomy

GRAIL 2017, MICGen 2017, MFCA 2017: Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics pp 201–209Cite as

Multi-modal Image Classification Using Low-Dimensional Texture Features for Genomic Brain Tumor Recognition

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10551)

Abstract

In this paper, we present a multi-modal medical image classification framework classifying brain tumor glioblastomas in genetic classes based on DNA methylation status. The framework makes use of computationally efficient 3D implementations of short local image descriptors, such as LBP, BRIEF and HOG, which are processed by a Bag-of-Patterns model to represent image regions, as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks. The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts.

Keywords

  • Medical Image Classification
  • Describe Image Regions
  • Autoencoder (AE)
  • BRIEF Features
  • Brain Tumor Dataset

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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Notes

  1. 1.

    https://bitbucket.org/s0216660/brain_tumor_segmentation_em.

  2. 2.

    Intel® Xeon® Processor E3-1225 v3.

  3. 3.

    GeForce GTX Titan X (VRAM 12 GB).

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Author information

Authors and Affiliations

  1. Neuroradiology, Klinikum Rechts der Isar, TU München, Munich, Germany

    Esther Alberts, Alexander Valentinitsch, Benedikt Wiestler & Claus Zimmer

  2. Department of Computer Science, TU München, Munich, Germany

    Esther Alberts, Giles Tetteh, Stefano Trebeschi, Marie Bieth, Alexander Valentinitsch & Bjoern H. Menze

  3. Institute for Advanced Study, TU München, Munich, Germany

    Esther Alberts & Bjoern H. Menze

Authors
  1. Esther Alberts
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  2. Giles Tetteh
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  3. Stefano Trebeschi
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  4. Marie Bieth
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  5. Alexander Valentinitsch
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  6. Benedikt Wiestler
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  7. Claus Zimmer
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  8. Bjoern H. Menze
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Corresponding author

Correspondence to Esther Alberts .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. Imperial College London, London, United Kingdom

    Enzo Ferrante

  4. Inria Sophia, Sophia-Antipolis, France

    Xavier Pennec

  5. Harvard Medical School, Massachusetts Institute of Technology, Boston, Massachusetts, USA

    Adrian V. Dalca

  6. Imperial College London, London, United Kingdom

    Sarah Parisot

  7. University of Utah, Salt Lake City, Utah, USA

    Sarang Joshi

  8. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Nematollah K. Batmanghelich

  9. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Dr. Aristeidis Sotiras

  10. University of Copenhagen, Copenhagen, Denmark

    Mads Nielsen

  11. Cornell University, Ithaca, New York, USA

    Mert R. Sabuncu

  12. University of Utah, Salt Lake City, USA

    Tom Fletcher

  13. University of Indiana, Indianapolis, Indiana, USA

    Li Shen

  14. Brain and Spine Institute, Inria, Paris, France

    Stanley Durrleman

  15. University of Copenhagen, Copenhagen, Denmark

    Stefan Sommer

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Alberts, E. et al. (2017). Multi-modal Image Classification Using Low-Dimensional Texture Features for Genomic Brain Tumor Recognition. In: Cardoso, M., et al. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics. GRAIL MICGen MFCA 2017 2017 2017. Lecture Notes in Computer Science(), vol 10551. Springer, Cham. https://doi.org/10.1007/978-3-319-67675-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-67675-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67674-6

  • Online ISBN: 978-3-319-67675-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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