Skip Navigation LinksHome > September 15, 2013 - Volume 38 - Issue 20 > Automatic Cobb Angle Determination From Radiographic Images
doi: 10.1097/BRS.0b013e3182a0c7c3

Automatic Cobb Angle Determination From Radiographic Images

Sardjono, Tri Arief PhD*; Wilkinson, Michael H. F. PhD; Veldhuizen, Albert G. PhD; van Ooijen, Peter M. A. PhD§; Purnama, Ketut E. PhD; Verkerke, Gijsbertus J. PhD‖,**

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Study Design. Automatic measurement of Cobb angle in patients with scoliosis.

Objective. To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images.

Summary of Background Data. Thirty-six frontal radiographical images of patients with scoliosis.

Methods. 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.

Results. 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°).

Conclusion. 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

© 2013 by Lippincott Williams & Wilkins

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