Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis
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.
KeywordsPrincipal Component Analysis Idiopathic Scoliosis Cobb Angle Spinal Deformity Canonical Correlation Analysis
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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