Robust Supervoxel Matching Combining Mid-Level Spectral and Context-Rich Features

  • Florian Tilquin
  • Pierre-Henri ConzeEmail author
  • Patrick Pessaux
  • Mathieu Lamard
  • Gwenolé Quellec
  • Vincent Noblet
  • Fabrice Heitz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)


This paper presents an innovative way to reach accurate semi-dense registration between images based on robust matching of structural entities. The proposed approach relies on a decomposition of images into visual primitives called supervoxels generated by aggregating adjacent voxels sharing similar characteristics. Two new categories of features are estimated at the supervoxel extent: mid-level spectral features relying on a spectral method applied on supervoxel graphs to capture the non-linear modes of intensity displacements, and mid-level context-rich features describing the broadened spatial context on the resulting spectral representations. Accurate supervoxel pairings are established by nearest neighbor search on these newly designed features. The effectiveness of the approach is demonstrated against state-of-the-art methods for semi-dense longitudinal registration of abdominal CT images, relying on liver label propagation and consistency assessment.


Semi-dense image registration Supervoxel matching Mid-level representation Laplacian graph Spectral decomposition Context-rich features 



This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from Investissements d’Avenir program). We acknowledge Visible Patient,, for 3D liver segmentation masks.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Florian Tilquin
    • 1
  • Pierre-Henri Conze
    • 2
    • 3
    Email author
  • Patrick Pessaux
    • 4
  • Mathieu Lamard
    • 3
    • 5
  • Gwenolé Quellec
    • 3
  • Vincent Noblet
    • 1
  • Fabrice Heitz
    • 1
  1. 1.ICube UMR 7357, Université de Strasbourg, CNRS, FMTSStrasbourgFrance
  2. 2.IMT AtlantiqueBrestFrance
  3. 3.Inserm, LaTIM UMR 1101BrestFrance
  4. 4.Institut Hospitalo-Universitaire de StrasbourgStrasbourgFrance
  5. 5.Université de Bretagne OccidentaleBrestFrance

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