Medical & Biological Engineering & Computing

, Volume 48, Issue 2, pp 185–195 | Cite as

Computer-aided assessment of scoliosis on posteroanterior radiographs

  • Junhua Zhang
  • Edmond Lou
  • Douglas L. Hill
  • James V. Raso
  • Yuanyuan Wang
  • Lawrence H. Le
  • Xinling Shi
Original Article

Abstract

In order to reduce the observer variability in radiographic scoliosis assessment, a computer-aided system was developed. The system semi-automatically measured the Cobb angle and vertebral rotation on posteroanterior radiographs based on Hough transform and snake model, respectively. Both algorithms were integrated with the shape priors to improve the performance. The system was tested twice by each of three observers. The intraobserver and interobserver reliability analyses resulted in the intraclass correlation coefficients higher than 0.9 and 0.8 for Cobb measurement on 70 radiographs and rotation measurement on 156 vertebrae, respectively. Both the Cobb and rotation measurements resulted in the average intraobserver and interobserver errors less than 2° and 3°, respectively. There were no significant differences in the measurement variability between groups of curve location, curve magnitude, observer experience, and vertebra location. Compared with the documented results, measurement variability is reduced by using the developed system. This system can help orthopedic surgeons assess scoliosis more reliably.

Keywords

Scoliosis Cobb angle Vertebral rotation Computer-aided measurement Radiograph 

References

  1. 1.
    Ajemba PO, Durdle NG, Hill D et al (2007) Classifying torso deformity in scoliosis using orthogonal maps of the torso. Med Biol Eng Comput 45:575–584CrossRefGoogle Scholar
  2. 2.
    Ajemba PO, Durdle NG, James Raso V (2008) Clinical monitoring of torso deformities in scoliosis using structured splines models. Med Biol Eng Comput 46:1201–1208CrossRefGoogle Scholar
  3. 3.
    Allen S, Parent E, Khorasani M et al (2008) Validity and reliability of active shape models for the estimation of Cobb angle in patients with adolescent idiopathic scoliosis. J Digit Imaging 21:208–218CrossRefGoogle Scholar
  4. 4.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–714CrossRefGoogle Scholar
  5. 5.
    Carman DL, Browne RH, Birch JG (1990) Measurement of scoliosis and kyphosis radiographs. Intraobserver and interobserver variation. J Bone Joint Surg Am 72:328–333Google Scholar
  6. 6.
    Chi WM, Cheng CW, Yeh WC et al (2006) Vertebral axial rotation measurement method. Comput Methods Programs Biomed 81:8–17Google Scholar
  7. 7.
    Chockalingam N, Dangerfield PH, Giakas G et al (2002) Computer-assisted Cobb measurement of scoliosis. Eur Spine J 11:353–357CrossRefGoogle Scholar
  8. 8.
    Cobb JR (1948) Outline for the study of scoliosis. Am Acad Orthop Surg Inst Course Lect 5:261–275Google Scholar
  9. 9.
    Colwell HR (1990) Radiographic measurements and clinical decisions. J Bone Joint Surg Am 72:319Google Scholar
  10. 10.
    Currier DP (1990) Elements of research in physical therapy. Williams and Wilkins, BaltimoreGoogle Scholar
  11. 11.
    Drerup B (1984) Principles of measurement of vertebral rotation from frontal projections of the pedicles. J Biomech 17:923–935CrossRefGoogle Scholar
  12. 12.
    Dumas R, Blanchard B, Carlier R et al (2008) A semi-automated method using interpolation and optimization for the 3D reconstruction of the spine from bi-planar radiography: a precision and accuracy study. Med Biol Eng Comput 46:85–92CrossRefGoogle Scholar
  13. 13.
    Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipse. IEEE Trans Pattern Anal Mach Intell 21:476–480CrossRefGoogle Scholar
  14. 14.
    Han JH, Koczy LT, Poston T (1994) Fuzzy Hough transform. Pattern Recogn Lett 15:649–658CrossRefGoogle Scholar
  15. 15.
    Hough PVC (1962) Method and means for recognizing complex patterns. U.S. Patent 3069654Google Scholar
  16. 16.
    Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15:850–863CrossRefGoogle Scholar
  17. 17.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1:321–331CrossRefGoogle Scholar
  18. 18.
    Loder RT, Spiegel D, Gutknecht S et al (2004) The assessment of intraobserver and interobserver error in the measurement of noncongenital scoliosis in children ≤10 years of age. Spine 29:2548–2553CrossRefGoogle Scholar
  19. 19.
    Lonstein JE (1994) Adolescent idiopathic scoliosis. Lancet 344:1407–1412CrossRefGoogle Scholar
  20. 20.
    Lonstein JE, Carlson JM (1984) The prediction of curve progression in untreated idiopathic scoliosis during growth. J Bone Joint Surg Am 66:1061–1071Google Scholar
  21. 21.
    Mok JM, Berven SH, Diab M et al (2008) Comparison of observer variation in conventional and three digital radiographic methods used in the evaluation of patients with adolescent idiopathic scoliosis. Spine 33:681–686CrossRefGoogle Scholar
  22. 22.
    Morrissy RT, Goldsmith GS, Hall EC et al (1990) Measurement of the Cobb angle on radiographs of patients who have scoliosis. Evaluation of intrinsic error. J Bone Joint Surg Am 72:320–327Google Scholar
  23. 23.
    Nash CL, Moe JH (1969) A study of vertebral rotation. J Bone Joint Surg Am 51:223–229Google Scholar
  24. 24.
    Pazos V, Cheriet F, Song L et al (2005) Accuracy assessment of human trunk surface 3D reconstructions from an optical digitising system. Med Biol Eng Comput 43:11–15CrossRefGoogle Scholar
  25. 25.
    Pluempitiwiriyawej C, Moura JMF, Wu YL et al (2005) STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans Med Imaging 24:593–603CrossRefGoogle Scholar
  26. 26.
    Pruijs JEH, Hageman MAPE, Keessen W et al (1994) Variation in Cobb angle measurements in scoliosis. Skeletal Radiol 23:517–520CrossRefGoogle Scholar
  27. 27.
    Rogala EJ, Drummond DS, Gurr J (1978) Scoliosis: incidence and natural history. A prospective epidemiological study. J Bone Joint Surg Am 60:173–176Google Scholar
  28. 28.
    Stokes IAF, Bigalow LC, Moreland MS (1986) Measurement of axial rotation of vertebrae in scoliosis. Spine 11:213–218CrossRefGoogle Scholar
  29. 29.
    Xie J, Jiang Y, Tsui H (2005) Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging 24:45–57CrossRefGoogle Scholar
  30. 30.
    Xu C, Prince J (1998) Snake, shapes, and gradient vector flow. IEEE Trans Image Process 7:359–369MATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Yawn BP, Yawn RA, Hodge D et al (1999) A population-based study of school scoliosis screening. JAMA 282:1427–1432CrossRefGoogle Scholar
  32. 32.
    Zhang J, Lou E, Le L et al (2008) Automatic Cobb measurement of scoliosis based on fuzzy Hough transform with vertebral shape prior. J Digit Imaging 22:463–472Google Scholar
  33. 33.
    Zhang J, Lou E, Le L et al (2008) Computer-assisted Cobb angle measurement on posteroanterior radiographs. Stud Health Technol Inform 140:151–156Google Scholar
  34. 34.
    Zoller T, Buhmann JM (2007) Robust image segmentation using resampling and shape constraints. IEEE Trans Pattern Anal Mach Intell 29:1147–1164CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2009

Authors and Affiliations

  • Junhua Zhang
    • 1
  • Edmond Lou
    • 2
  • Douglas L. Hill
    • 2
  • James V. Raso
    • 2
  • Yuanyuan Wang
    • 3
  • Lawrence H. Le
    • 4
  • Xinling Shi
    • 1
  1. 1.Department of Electronic EngineeringYunnan UniversityKunmingChina
  2. 2.Department of Rehabilitation TechnologyGlenrose Rehabilitation HospitalEdmontonCanada
  3. 3.Department of Electronic EngineeringFudan UniversityShanghaiChina
  4. 4.Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonCanada

Personalised recommendations