International Journal of Computer Vision

, Volume 101, Issue 1, pp 161–183 | Cite as

Sparse Adaptive Parameterization of Variability in Image Ensembles

  • Stanley Durrleman
  • Stéphanie Allassonnière
  • Sarang Joshi


This paper introduces a new parameterization of diffeomorphic deformations for the characterization of the variability in image ensembles. Dense diffeomorphic deformations are built by interpolating the motion of a finite set of control points that forms a Hamiltonian flow of self-interacting particles. The proposed approach estimates a template image representative of a given image set, an optimal set of control points that focuses on the most variable parts of the image, and template-to-image registrations that quantify the variability within the image set. The method automatically selects the most relevant control points for the characterization of the image variability and estimates their optimal positions in the template domain. The optimization in position is done during the estimation of the deformations without adding any computational cost at each step of the gradient descent. The selection of the control points is done by adding a L 1 prior to the objective function, which is optimized using the FISTA algorithm.


Atlas construction Image variability Diffeomorphisms Sparsity Control points FISTA 



We would like to thank Timothy O’Keefe and Paul Sanders for their kind proofreading of the manuscript. This work has been supported by ANR grant IRMGroup and NIH grants NIBIB (5R01EB007688) and NCRR (2P41 RR0112553-12).


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Stanley Durrleman
    • 1
  • Stéphanie Allassonnière
    • 2
  • Sarang Joshi
    • 1
  1. 1.Scientific Computing and Imaging (SCI) InstituteSalt Lake CityUSA
  2. 2.Centre de Mathématiques Appliquées (CMAP), UMR CNRS 7641Ecole PolytechniquePalaiseauFrance

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