Entropy-Based Automatic Segmentation of Bones in Digital X-ray Images

  • Oishila Bandyopadhyay
  • Bhabatosh Chanda
  • Bhargab B. Bhattacharya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


Bone image segmentation is an integral component of orthopedic X-ray image analysis that aims at extracting the bone structure from the muscles and tissues. Automatic segmentation of the bone part in a digital X-ray image is a challenging problem because of its low contrast with the surrounding flesh, which itself needs to be discriminated against the background. The presence of noise and spurious edges further complicates the segmentation. In this paper, we propose an efficient entropy-based segmentation technique that integrates several simple steps, which are fully automated. Experiments on several X-ray images reveal encouraging results as evident from a segmentation entropy quantitative assessment (SEQA) metric [Hao, et al. 2009].


Entropy Digital X-ray LOCO Medical imaging Segmentation 


  1. 1.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evolution. Journal of Electronic Imaging 13(1), 146–165 (2004)CrossRefGoogle Scholar
  2. 2.
    Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (1998)CrossRefGoogle Scholar
  3. 3.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  4. 4.
    Yang, J., Staib, L.H., Duncan, V.: Neighborhood-constrained segmentation with level based 3-D deformable models. IEEE Transactions on Medical Imaging 23(8), 940–948 (2004)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson, London (2008)Google Scholar
  6. 6.
    Šćepanović, D., Kirshtein, J., Jain, A.K., Taylor, R.H.: Fast algorithm for probabilistic bone edge detection (FAPBED). In: SPIE, vol. 5747, pp. 1753–1765 (2005)Google Scholar
  7. 7.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  8. 8.
    Sen, D., Pal, S.K.: Gradient histogram thresholding in a region of interest for edge detection. Image and Vision Computing 28, 677–695 (2010)CrossRefGoogle Scholar
  9. 9.
    Kundu, M.K., Pal, S.K.: Thresholding for edge detection using human psycho-visual phenomena. Pattern Recognition Letters 4(6), 433–441 (1986)CrossRefGoogle Scholar
  10. 10.
    Pal, S.K., King, R.A.: On edge detection of X-ray images using fuzzy set. IEEE Transactions on Pattern Analysis and Machine Intelligence 5, 69–77 (1983)CrossRefGoogle Scholar
  11. 11.
    Schulze, M.A., Pearce, J.A.: Linear combinations of morphological operators: The midrange, pseudomedian, and LOCO filters. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 57–60 (1993)Google Scholar
  12. 12.
    Ning, J.L., Zhang, J., Zhang, D., Wu, C.: Interactive image segmentation by maximal similarity based region merging. Pattern Recognition 43, 445–456 (2010)CrossRefzbMATHGoogle Scholar
  13. 13.
    Yan, C., Sang, N., Zhang, T.: Local entropy-based transition region extraction and thresholding. Pattern Recognition Letters 24, 2935–2941 (2003)CrossRefGoogle Scholar
  14. 14.
    Kang, W., Wang, K., Wang, Q., An, D.: Segmentation method based on transition region extraction for coronary angiograms. In: IEEE International Conference on Mechatronics and Automation, pp. 905–909 (2009)Google Scholar
  15. 15.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 23(9), 1277–1294 (1993)CrossRefGoogle Scholar
  16. 16.
    Hao, J., Shen, Y., Xu, H., Zou, J.: A region entropy based objective evaluation method for image segmentation. In: IEEE International Conference on Instrumentation and Measurement Technology, pp. 373–377 (2009)Google Scholar
  17. 17.
    Zhang, Y.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)CrossRefGoogle Scholar
  18. 18.
    Ding, F.: Segmentation of bone structure in X-ray images. Thesis Proposal, School of Computing, National University of Singapore (2006)Google Scholar
  19. 19.
    Liang, J., Pan, B.-C., Fan, Y.-H.: Fracture identification of X-ray image. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 67–73 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Oishila Bandyopadhyay
    • 1
  • Bhabatosh Chanda
    • 2
  • Bhargab B. Bhattacharya
    • 2
  1. 1.Department of CSECamellia Institute of TechnologyKolkataIndia
  2. 2.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

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