Longitudinal Scoliotic Trunk Analysis via Spectral Representation and Statistical Analysis

  • Ola Ahmad
  • Herve Lombaert
  • Stefan Parent
  • Hubert Labelle
  • Jean Dansereau
  • Farida Cheriet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)


Scoliosis is a complex 3D deformation of the spine leading to asymmetry of the external shape of the human trunk. A clinical follow-up of this deformation is decisive for its treatment, which depends on the spinal curvature but also on the deformity’s progression over time. This paper presents a new method for longitudinal analysis of scoliotic trunks based on spectral representation of shapes combined with statistical analysis. The spectrum of the surface model is used to compute the correspondence between deformable scoliotic trunks. Spectral correspondence is combined with Canonical Correlation Analysis to do point-wise feature comparison between models. This novel combination allows us to efficiently capture within-subject shape changes to assess scoliosis progression (SP). We tested our method on 23 scoliotic patients with right thoracic curvature. Quantitative comparison with spinal measurements confirms that our method is able to identify significant changes associated with SP.


Cobb Angle Canonical Correlation Analysis Canonical Variate Radial Basis Function Weighted Adjacency Matrix 
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.



This research was funded by the Canadian Institutes of Health Research (grant number MPO 125875). The authors would like to thank Philippe Debanné for revising this paper and the anonymous reviewers for their insightful comments and suggestions.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ola Ahmad
    • 1
    • 2
  • Herve Lombaert
    • 3
  • Stefan Parent
    • 1
    • 2
  • Hubert Labelle
    • 1
    • 2
  • Jean Dansereau
    • 2
    • 4
  • Farida Cheriet
    • 2
    • 4
  1. 1.Université de MontréalMontréalCanada
  2. 2.Centre de Recherche du CHU Sainte-JustineMontréalCanada
  3. 3.INRIASophia-antipolisFrance
  4. 4.École Polytechnique de MontréalMontréalCanada

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