A Semi-Automatic Algorithm for Estimating Cobb Angle

Abstract

Background: Scoliosis is the most common type of spinal deformity. A universal and standard method for evaluating scoliosis is Cobb angle measurement, but several studies have shown that there is intra- and inter- observer variation in measuring cobb angle manually.

Objective: Develop a computer- assisted system to decrease operator-dependent errors in Cobb angle measurement.

Methods: The spinal cord in the given x-ray image of the spine is highlighted using contract-stretching technique. The overall structural curvature of the spine is determined by a semi-automatic algorithm aided by the operator. Once the morphologic curve of the spine is determined, in the last step the cobb-angle is estimated by calculating the angle between two normal lines to the spinal curve at the inflection points of the curve.

Results: Evaluation results of the developed algorithms using 14 radiographs of patients ( 4 - 40 years old) with cobb angle ranges from 34 - 82 degrees, revealed that the developed algorithm accurately estimated cobb angle. Statistical analysis showed that average angle values estimated using the developed method and that provided by experts are statistically equal. The correlation coefficient between the angle values estimated using the developed algorithm and those provided by the expert is 0.81.

Conclusion: Compared with previous algorithms, the developed system is easy to use, less operator-dependent, accurate, and reliable. The obtained results are promising and show that the developed computer-based system could be used to quantify scoliosis by measuring Cobb angle.

Keywords: Curve-fitting; Scoliosis; Spinal Curvature Measurement; Cobb-angle Measurement.

Figure1

Comparison of a healthy patient with one with scoliosis (available from: http://www.sierraneurosurgery.com/procedures-and-expertise/adult-scoliosis/.)

Figure2

Manual method for measuring the Cobb-angle (available from: https://www.spinemd.com/news-philanthropy/scoliosis-measurements).

Figure3

Phases of Cobb-angle estimation

Figure4

Applying piecewise linear transformation functions to image brightness [22].

Figure5

a) original image (left), b) processed image (right)

Figure6

Example of a 5-th order polynomial curve fitting for the spinal curvature

Figure7

Cobb-angle measurement using tangent equations

Figure8

Type of spinal curvature: more than one y for one x