Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis
2014 (English)In: Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan / [ed] Jianhua Yao,Tobias Klinder, Shuo Li, Springer, 2014, Vol. 17, 123-134 p.Conference paper (Refereed)
In this paper, we propose the concept of eigenspine, a data analysis schemeuseful for quantifying the linear correlation between different measures relevant fordescribing 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 thenumber of dimensions in the measurement space, thereby providing a regularizationof the measurements, and where CCA is used to determine the linear dependence betweenpair-wise combinations of the different measures. To demonstrate the usefulnessof the eigenspine concept, the measures describing position and rotation of thelumbar and the thoracic vertebrae of 22 patients suffering from idiopathic scoliosiswere analyzed. The analysis showed that the strongest linear relationship is foundbetween the anterior-posterior displacement and the sagittal rotation of the vertebrae,and that a somewhat weaker but still strong correlation is found between thelateral displacement and the frontal rotation of the vertebrae. These results are wellin-line with the general understanding of idiopathic scoliosis. Noteworthy though isthat the obtained results from the analysis further proposes axial vertebral rotationas a differentiating measure when characterizing idiopathic scoliosis. Apart fromanalyzing pair-wise linear correlations between different measures, the method isbelieved to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.
Place, publisher, year, edition, pages
Springer, 2014. Vol. 17, 123-134 p.
, Lecture Notes in Computational Vision and Biomechanics, ISSN 2212-9391 ; 17
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-108975DOI: 10.1007/978-3-319-07269-2_11ISBN: 978-3-319-07268-5 (print)ISBN: 978-3-319-07269-2 (online)OAI: oai:DiVA.org:liu-108975DiVA: diva2:734286
16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan
FunderSwedish Research Council, 2007-4786Swedish Foundation for Strategic Research , SM10-0022