The Large Deformation Diffeomorphic Metric Mapping (LDDMM) tool is an application which aims to assign metric distances on the space of anatomical images in Computational Anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. As part of these efforts the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences.

References in zbMATH (referenced in 72 articles , 1 standard article )

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  1. Effland, Alexander; Neumayer, Sebastian; Rumpf, Martin: Convergence of the time discrete metamorphosis model on Hadamard manifolds (2020)
  2. Arnaudon, Alexis; Holm, Darryl D.; Sommer, Stefan: A geometric framework for stochastic shape analysis (2019)
  3. Chen, Chong; Gris, Barbara; ÖKtem, Ozan: A new variational model for joint image reconstruction and motion estimation in spatiotemporal imaging (2019)
  4. Kaltenmark, Irène; Trouvé, Alain: Estimation of a growth development with partial diffeomorphic mappings (2019)
  5. Mumford, David: A tribute to Ulf Grenander (2019)
  6. Tward, Daniel J.; Mitra, Partha P.; Miller, Michael I.: Estimating diffeomorphic mappings between templates and noisy data: variance bounds on the estimated canonical volume form (2019)
  7. Effland, Alexander; Rumpf, Martin; Schäfer, Florian: Image extrapolation for the time discrete metamorphosis model: existence and applications (2018)
  8. Gris, Barbara; Durrleman, Stanley; Trouvé, Alain: A sub-Riemannian modular framework for diffeomorphism-based analysis of shape ensembles (2018)
  9. Neumayer, Sebastian; Persch, Johannes; Steidl, Gabriele: Morphing of manifold-valued images inspired by discrete geodesics in image spaces (2018)
  10. Bauer, Martin; Joshi, Sarang; Modin, Klas: On geodesic completeness for Riemannian metrics on smooth probability densities (2017)
  11. Camassa, Roberto; Kuang, Dongyang; Lee, Long: A geodesic landmark shooting algorithm for template matching and its applications (2017)
  12. Charlier, B.; Charon, N.; Trouvé, A.: The fshape framework for the variability analysis of functional shapes (2017)
  13. Gerber, Samuel; Maggioni, Mauro: Multiscale strategies for computing optimal transport (2017)
  14. Arguillère, Sylvain; Trélat, Emmanuel; Trouvé, Alain; Younes, Laurent: Registration of multiple shapes using constrained optimal control (2016)
  15. Camassa, Roberto; Kuang, Dongyang; Lee, Long: Solitary waves and (N)-particle algorithms for a class of Euler-Poincaré equations (2016)
  16. Mittal, Rajat; Seo, Jung Hee; Vedula, Vijay; Choi, Young J.; Liu, Hang; Huang, H. Howie; Jain, Saurabh; Younes, Laurent; Abraham, Theodore; George, Richard T.: Computational modeling of cardiac hemodynamics: current status and future outlook (2016)
  17. Allassonnière, S.; Durrleman, S.; Kuhn, E.: Bayesian mixed effect atlas estimation with a diffeomorphic deformation model (2015)
  18. Allassonnière, Stéphanie; Kuhn, Estelle: Convergent stochastic expectation maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation (2015)
  19. Arguillère, Sylvain; Trélat, Emmanuel; Trouvé, Alain; Younes, Laurent: Shape deformation analysis from the optimal control viewpoint (2015)
  20. Bal, Gulce; Diebold, Julia; Chambers, Erin Wolf; Gasparovic, Ellen; Hu, Ruizhen; Leonard, Kathryn; Shaker, Matineh; Wenk, Carola: Skeleton-based recognition of shapes in images via longest path matching (2015)

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