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Proceedings Paper

Assessment of scoliosis by direct measurement of the curvature of the spine
Author(s): Geoff Dougherty; Michael J. Johnson
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Paper Abstract

We present two novel metrics for assessing scoliosis, in which the geometric centers of all the affected vertebrae in an antero-posterior (A-P) radiographic image are used. This is in contradistinction to the existing methods of using selected vertebrae, and determining either their endplates or the intersections of their diagonals, to define a scoliotic angle. Our first metric delivers a scoliotic angle, comparable to the Cobb and Ferguson angles. It measures the sum of the angles between the centers of the affected vertebrae, and avoids the need for an observer to decide on the extent of component curvatures. Our second metric calculates the normalized root-mean-square curvature of the smoothest path comprising piece-wise polynomial splines fitted to the geometric centers of the vertebrae. The smoothest path is useful in modeling the spinal curvature. Our metrics were compared to existing methods using radiographs from a group of twenty subjects with spinal curvatures of varying severity. Their values were strongly correlated with those of the scoliotic angles (r = 0.850 - 0.886), indicating that they are valid surrogates for measuring the severity of scoliosis. Our direct use of positional data removes the vagaries of determining variably shaped endplates, and circumvented the significant interand intra-observer errors of the Cobb and Ferguson methods. Although we applied our metrics to two-dimensional (2- D) data in this paper, they are equally applicable to three-dimensional (3-D) data. We anticipate that they will prove to be the basis for a reliable 3-D measurement and classification system.

Paper Details

Date Published: 27 February 2009
PDF: 10 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603Q (27 February 2009); doi: 10.1117/12.806655
Show Author Affiliations
Geoff Dougherty, California State Univ. Channel Islands (United States)
Michael J. Johnson, Kuwait Univ. (Kuwait)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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