Nonlinear Embedding towards Articulated Spine Shape Inference Using Higher-Order MRFs

  • Samuel Kadoury
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)


In this paper we introduce a novel approach for inferring articulated spine models from images. A low-dimensional manifold embedding is created from a training set of prior mesh models to establish the patterns of global shape variations. Local appearance is captured from neighborhoods in the manifold once the overall representation converges. Inference with respect to the manifold and shape parameters is performed using a Markov Random Field (MRF). Singleton and pairwise potentials measure the support from the data and shape coherence from neighboring models respectively, while higher-order cliques encode geometrical modes of variation for local vertebra shape warping. Optimization of model parameters is achieved using efficient linear programming and duality. The resulting model is geometrically intuitive, captures the statistical distribution of the underlying manifold and respects image support in the spatial domain. Experimental results on spinal column geometry estimation from CT demonstrate the approach’s potential.


Markov Random Field Local Shape Rigid Transformation Pairwise Potential Point Distribution Model 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Samuel Kadoury
    • 1
  • Nikos Paragios
    • 2
  1. 1.Philips Research North AmericaBriarcliff ManorUSA
  2. 2.Ecole Centrale de Paris, Laboratoire MAS, GALEN Group, INRIA Saclay, Ile-de-FranceFrance

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