Automatic measurement of Cobb angle in patients with scoliosis.
To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images.
Thirty-six frontal radiographical images of patients with scoliosis.
A modified charged particle model is used to determine the curvature on radiographical spinal images. Three curve fitting methods, piece-wise linear, splines, and polynomials, each with 3 variants were used and evaluated for the best fit. The Cobb angle was calculated out of these curve fit lines and compared with a manually determined Cobb angle. The best-automated method is determined on the basis of the lowest mean absolute error and standard deviation, and the highest R2.
The error of the manual Cobb angle determination among the 3 observers, determined as the mean of the standard deviations of all sets of measurements, was 3.37°. For the automatic method, the best piece-wise linear method is the 3-segments method. The best spline method is the 10-steps method. The best polynomial method is poly 6. Overall, the best automatic methods are the piece-wise linear method using 3 segments and the polynomial method using poly 6, with a mean absolute error of 4,26° and 3,91° a standard deviation of 3,44° and 3,60°, and a R2 of 0.9124 and 0.9175. The standard measurement error is significantly lower than the upper bound found in the literature (11.8°).
The automatic Cobb angle method seemed to be better than the manual methods described in the literature. The piece-wise linear method using 3 segments and the polynomial method using poly 6 yield the 2 best results because the mean absolute error, standard deviation, and R2 are the best of all methods.
Level of Evidence: 3
Supplemental Digital Content is Available in the Text.An automatic Cobb angle determination method of patients with scoliosis is proposed using a charged particle model. The Cobb angles were evaluated on the basis of the particle positions after the iterations were finished by using curve fitting methods. The best overall method is polynomial curve fitting.
*Institut Teknologi Sepuluh Nopember, Surabaya, Jawa Timur, Indonesia;
†Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands;
‡Department of Orthopaedic Surgery, University of Groningen, University Medical center Groningen, Groningen, The Netherlands;
§Department of Radiology, University of Groningen, University Medical center Groningen, Groningen, The Netherlands;
¶Department of Electrical Engineering, Institute of Technology Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia;
‖Department of Rehabilitation Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; and
**Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.
Address correspondence and reprints requests to Tri Arief Sardjono, PhD, Institut Teknologi Sepuluh Nopember, Surabaya, Jawa Timur, Indonesia; E-mail: firstname.lastname@example.org
Acknowledgment date: May 29, 2012. First revision date: May 10, 2013. Acceptance date: June 6, 2013.
The manuscript submitted does not contain information about medical device(s)/drug(s).
No funds were received in support of this work.
No relevant financial activities outside the submitted work.