Automated Bone Age Assessment Using Feature Extraction

  • Luke M. Davis
  • Barry-John Theobald
  • Anthony Bagnall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)


Bone age assessment is a task performed daily in hospitals worldwide, this involves a clinician estimating the age of a patient from a radiograph of the non-dominant hand. In this paper, we propose a combination of image processing and feature extraction algorithms to automatically predict the Tanner-Whitehouse bone stage, the assessment standard used in forming bone age estimates.


Feature Extraction Bone Age Assessment Medical Imaging 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tanner, J., Whitehouse, R., Marshall, W., Healy, M., Goldstein, H.: Assessment of skeletal maturity and prediction of adult height (TW2 method), vol. 16. Academic Press, London (1975)Google Scholar
  2. 2.
    Davis, L., Theobald, B.J., Toms, A., Bagnall, A.: On the Extraction and Classification of Hand Outlines. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 92–99. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Thodberg, H., Kreiborg, S., Juul, A., Pedersen, K.: The BoneXpert method for automated determination of skeletal maturity. IEEE Transactions on Medical Imaging 28(1), 52–66 (2009)CrossRefGoogle Scholar
  4. 4.
    Cootes, T., Edwards, G., Taylor, C.: Comparing active shape models with active appearance models. In: British Machine Vision Conference, vol. 1, pp. 173–183 (1999)Google Scholar
  5. 5.
    Niemeijer, M., van Ginneken, B., Maas, C., Beek, F., Viergever, M.: Assessing the skeletal age from a hand radiograph: automating the Tanner-Whitehouse method. In: SPIE Medical Imaging, vol. 5032, pp. 1197–1205 (2003)Google Scholar
  6. 6.
    Cao, F., Huang, H., Pietka, E., Gilsanz, V.: Digital hand atlas and web-based bone age assessment: system design and implementation. Computerized Medical Imaging and Graphics 24(5), 297–307 (2000)CrossRefGoogle Scholar
  7. 7.
    Adelson, E., Anderson, C., Bergen, J., Burt, P., Ogden, J.: Pyramid methods in image processing. RCA Engineer 29(6), 33–41 (1984)Google Scholar
  8. 8.
    Canny, J.: A computational approach to edge detection. Readings in Computer Vision: Issues, Problems, Principles, and Paradigms 184 (1987)Google Scholar
  9. 9.
    Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luke M. Davis
    • 1
  • Barry-John Theobald
    • 1
  • Anthony Bagnall
    • 1
  1. 1.School of Computing SciencesUniversity of East AngliaNorwichUK

Personalised recommendations