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Lossless PDE-based Compression of 3D Medical Images

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Scale Space and Variational Methods in Computer Vision (SSVM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12679))

Abstract

Inpainting with Partial Differential Equations (PDEs) has previously been used as a basis for lossy image compression. For medical images, lossless compression is often considered to be safer, given that even subtle details could be diagnostically relevant. In this work, we introduce a PDE-based codec that achieves competitive compression rates for lossless image compression. It is based on coding the differences between the original image and its PDE-based reconstruction. These differences often have lower entropy than the original image, and can therefore be coded more efficiently. We optimize this idea via an iterative reconstruction scheme, and a separate coding of empty space, which takes up a considerable fraction of the field of view in many 3D medical images. We demonstrate that our PDE-based codec compares favorably to previously established lossless codecs. We also investigate the individual benefit from each ingredient of our codec on multiple examples, explore the effect of using homogeneous, edge enhancing, and fourth-order anisotropic diffusion, and discuss the choice of contrast parameters.

Supported by the German Academic Exchange Service (DAAD). The brain MR images were kindly provided by Tobias Schmidt-Wilcke, University of Düsseldorf. The foot CT dataset is courtesy of Philips Research.

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Authors and Affiliations

  1. University of Bonn, Bonn, Germany

    Ikram Jumakulyyev & Thomas Schultz

Authors
  1. Ikram Jumakulyyev
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  2. Thomas Schultz
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Corresponding author

Correspondence to Thomas Schultz .

Editor information

Editors and Affiliations

  1. UNICAEN, GREYC – Normandy University, Caen, France

    Abderrahim Elmoataz

  2. ENSICAEN, GREYC – Normandy University, Caen, France

    Jalal Fadili

  3. CNRS, GREYC – Normandy University, Caen, France

    Yvain Quéau

  4. UNICAEN, GREYC – Normandy University, Caen, France

    Julien Rabin

  5. ENSICAEN, GREYC – Normandy University, Caen, France

    Loïc Simon

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Jumakulyyev, I., Schultz, T. (2021). Lossless PDE-based Compression of 3D Medical Images. In: Elmoataz, A., Fadili, J., Quéau, Y., Rabin, J., Simon, L. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science(), vol 12679. Springer, Cham. https://doi.org/10.1007/978-3-030-75549-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-75549-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75548-5

  • Online ISBN: 978-3-030-75549-2

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