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Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis

  • Daniel ForsbergEmail author
  • Claes Lundström
  • Mats Andersson
  • Hans Knutsson
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)

Abstract

In this paper, we propose the concept of eigenspine, a data analysis scheme useful for quantifying the linear correlation between different measures relevant for describing spinal deformities associated with spinal diseases, such as idiopathic scoliosis. The proposed concept builds upon the use of principal component analysis (PCA) and canonical correlation analysis (CCA), where PCA is used to reduce the number of dimensions in the measurement space, thereby providing a regularization of the measurements, and where CCA is used to determine the linear dependence between pair-wise combinations of the different measures. To demonstrate the usefulness of the eigenspine concept, the measures describing position and rotation of the lumbar and the thoracic vertebrae of 22 patients suffering from idiopathic scoliosis were analyzed. The analysis showed that the strongest linear relationship is found between the anterior-posterior displacement and the sagittal rotation of the vertebrae, and that a somewhat weaker but still strong correlation is found between the lateral displacement and the frontal rotation of the vertebrae. These results are well in-line with the general understanding of idiopathic scoliosis. Noteworthy though is that the obtained results from the analysis further proposes axial vertebral rotation as a differentiating measure when characterizing idiopathic scoliosis. Apart from analyzing pair-wise linear correlations between different measures, the method is believed to be suitable for finding a maximally descriptive low-dimensional combination of measures describing spinal deformities in idiopathic scoliosis.

Keywords

Principal Component Analysis Idiopathic Scoliosis Cobb Angle Spinal Deformity Canonical Correlation Analysis 
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.

Notes

Acknowledgments

The authors would like to thank L. Vavruch and H. Tropp at the Department of Clinical and Experimental Medicine, Linköping University, Sweden, for valuable input in discussions regarding idiopathic scoliosis and for assistance in collecting the used image data. This work was funded by the Swedish Research Council (grant 2007-4786) and the Swedish Foundation for Strategic Research (grant SM10-0022).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel Forsberg
    • 1
    • 3
    Email author
  • Claes Lundström
    • 1
    • 3
  • Mats Andersson
    • 1
    • 2
  • Hans Knutsson
    • 1
    • 2
  1. 1.Center for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
  2. 2.Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
  3. 3.SectraLinköpingSweden

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