Document Type : Original Article

Authors

1 Ph.D., Assistant Professor, Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran

2 Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

3 Msc, Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran

4 Department of Orthopedics, Shiraz University of Medical Sciences, Shiraz, Iran

Abstract

Introduction: Scoliosis is a musculoskeletal disorder in which a person's spine bends sideways and rotates along its vertical axis. Cobb-angle (CA) is a common index to evaluate, determine, and track the progression of scoliosis; however, measuring this index manually depends on the operator’s experience and image quality, which may lead to errors, dispersion of obtained values, and variability. This study aimed to resolve this issue by developing an algorithm for measuring CA.
Methods: In this analytical study, we developed software using image processing, data modeling, and analytical geometry tools for measuring CA.  A given x-ray image was processed to highlight the spinal cord. Then, the spinal cord curvature was extracted by manually segmenting the spinal cord in the given image and fitting a polynomial function to the data points in the identified region.  Finally, CA was estimated by calculating the angle between the two normal lines that pass through the inflection points of the fitted curve. Thirty X-ray images having CA values obtained by an expert were used to evaluate the accuracy of the developed algorithm.
Results: There is no statistically significant difference between the average CA values measured by the expert (53.95º ± 20.3º) and those estimated using the developed software (56.41º ± 19.95º). There is a significant correlation between the values estimated using the developed algorithm and the reference values, (r=0.93) and (P<10-8).
Conclusion: The obtained results are promising and show that the developed algorithm might be used to measure cobb-angle. Nevertheless, its accuracy and reliability should be further evaluated using a large data set.

Keywords

  1. Adam C, Dougherty G. Applications of Medical Image Processing in the Diagnosis and Treatment of Spinal Deformity. In 2011. p. 227–48.
  2. Jaeger UE, Koenig RS, Gieseke J, Wagner U, Kandyba J, Ostertun B. MR total spine projection in juvenile scoliosis: an alternative to radiographic follow-up. InRadiology 1998 Nov 1 (Vol. 209, pp. 402-402). 20th and Northampton STS, Easton, PA 18042 USA: radiological soc north amer.
  3. Benli IT, Akalin S, Aydin E, Baz A, Çitak M, Kiş M, et al. Isola spinal instrumentation system for idiopathic scoliosis. Arch Orthop Trauma Surg. 2001;121(1–2):17–25.
  4. Gstoettner M, Sekyra K, Walochnik N, Winter P, Wachter R, Bach CM. Inter- and intraobserver reliability assessment of the Cobb angle: Manual versus digital measurement tools. Eur Spine J. 2007 Oct;16(10):1587–92.
  5. Carman DL, Browne RH, Birch JG. Measurement of scoliosis and kyphosis radiographs. Intraobserver and interobserver variation. The Journal of bone and joint surgery. American volume. 1990 Mar;72(3):328-33.
  6. Koenig R, Jaeger U, Ostertun B, Kandyba J, Wagner U, Gieseke J, Schild HH. MR whole-spine recording: computer-assisted simulation of the conventional x-ray technic. RoFo: Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin. 1999 Mar;170(3):258-61.
  7. Morrissy RT, Goldsmith GS, Hall EC, Kehl D, Cowie GH. Measurement of the Cobb angle on radiographs of patients who have. J Bone Joint Surg Am. 1990;72(3):320-7.
  8. Kundu R, Chakrabarti A, Lenka PK. Cobb angle measurement of scoliosis with reduced variability. arXiv preprint arXiv:1211.5355. 2012 Nov 22.
  9. Facanha-Filho FA, Winter RB, Lonstein JE, Koop S, Novacheck T, L’Heureux EA, Noren CA. Measurement accuracy in congenital scoliosis. JBJS. 2001 Jan 1;83(1):42.
  10. Oda M, Rauh S, Gregory PB, Silverman FN, Bleck EE. The significance of roentgenographic measurement in scoliosis. Journal of Pediatric Orthopaedics. 1982 Oct 1;2(4):378-82.
  11. Allen S, Parent E, Khorasani M, Hill DL, Lou E, Raso JV. Validity and reliability of active shape models for the estimation of Cobb angle in patients with adolescent idiopathic scoliosis. Journal of Digital Imaging. 2008 Jun 1;21(2):208-18.
  12. Jeffries BF, Tarlton M, De Smelt AA. Computerized measurement and analysis of scoliosis. A more accurate representation of the shape of the curve. Radiology. 1980;134(2):381–5.
  13. Rosenfeldt MP, Harding IJ, Hauptfleisch JT, Fairbank JT. A comparison of traditional protractor versus Oxford Cobbometer radiographic measurement: intraobserver measurement variability for Cobb angles. Spine. 2005 Feb 15;30(4):440-3.
  14. Chockalingam N, Dangerfield PH, Giakas G, Cochrane T, Dorgan JC. Computer-assisted Cobb measurement of scoliosis. European Spine Journal. 2002 Aug 1;11(4):353-7.
  15. Safari A, Parsaei H, Zamani A, Pourabbas B. A Semi-Automatic Algorithm for Estimating Cobb Angle. J Biomed Phys Eng. 2019 Jun 1;9(3):317–26.
  16. Zhang J, Lou E, Le LH, Hill DL, Raso JV, Wang Y. Automatic Cobb measurement of scoliosis based on fuzzy Hough transform with vertebral shape prior. Journal of Digital Imaging. 2009 Oct 1;22(5):463.
  17. Omoto E, Wakamatsu O, Sanada S. Development of software for automatic measurement of Cobb angle and quantitative assessment method for follow-up in radiographs of patients with scoliosis.
  18. Duong L, Cheriet F, Labelle H. Automatic detection of scoliotic curves in posteroanterior radiographs. IEEE transactions on biomedical engineering. 2010 Feb 5;57(5):1143-51.
  19. Andrews HC. Digital Image Processing. Vol. 7, Computer. 1974. p.17–19
  20. Chockalingam N, Dangerfield PH, Giakas G, Cochrane T, Dorgan JC. Computer-assisted Cobb measurement of scoliosis. Eur Spine J. 2002;11(4):353–7.
  21. Tanure MC, Pinheiro AP, Oliveira AS. Reliability assessment of Cobb angle measurements using manual and digital methods. The Spine Journal. 2010 Sep 1;10(9):769-74.
  22. Kundu R, Lenka P, Kumar R, Chakrabarti A. Cobb angle quantification for scoliosis using image processing techniques. Proceedings of the International Journal of Computer Applications. 2012 Apr:6-11.
  23. Alharbi RH, Alshaye MB, Alkanhal MM, Alharbi NM, Alzahrani MA, Alrehaili OA. Deep Learning Based Algorithm For Automatic Scoliosis Angle Measurement. In2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) 2020 Mar 19 (pp. 1-5). IEEE.
  24. Papaliodis DN, Bonanni PG, Roberts TT, Hesham K, Richardson N, Cheney RA, Lawrence JP, Carl AL, Lavelle WF. Computer assisted Cobb angle measurements: a novel algorithm. International Journal of Spine Surgery. 2017 Jan 1;11(3).
  25. Gstoettner M, Sekyra K, Walochnik N, Winter P, Wachter R, Bach CM. Inter- and intraobserver reliability assessment of the Cobb angle: Manual versus digital measurement tools. Eur Spine J. 2007 Oct;16(10):1587–92.
  26. Tanure MC, Pinheiro AP, Oliveira AS. Reliability assessment of Cobb angle measurements using manual and digital methods. Spine J. 2010 Sep 1;10(9):769–74.