Advertisement

Long-Bone Fracture Detection in Digital X-ray Images Based on Concavity Index

  • Oishila Bandyopadhyay
  • Arindam Biswas
  • Bhargab B. Bhattacharya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8466)

Abstract

Fracture detection is a crucial part in orthopedic X-ray image analysis. Automated fracture detection for the patients of remote areas is helpful to the paramedics for early diagnosis and to start an immediate medical care. In this paper, we propose a new technique of automated fracture detection for long-bone X-ray images based on digital geometry. The method can trace the bone contour in an X-ray image and can identify the fracture locations by utilizing a novel concept of concavity index of the contour. It further uses a new concept of relaxed digital straight line (RDSS) for restoring the false contour discontinuities that may arise due to segmentation or contouring error. The proposed method eliminates the shortcomings of earlier fracture detection approaches that are based on texture analysis or use training sets. Experiments with several digital X-ray images reveal encouraging results.

Keywords

Medical imaging Bone X-ray Chain code Digital straight line segment (DSS) Approximate digital straight line segment (ADSS) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bandyopadhyay, O., Biswas, A., Chanda, B., Bhattacharya, B.B.: Bone contour tracing in digital X-ray images based on adaptive thresholding. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 465–473. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Bandyopadhyay, O., Chanda, B., Bhattacharya, B.B.: Entropy-based automatic segmentation of bones in digital X-ray images. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 122–129. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Bhowmick, P., Bhattacharya, B.B.: Fast polygonal approximation of digital curves using relaxed straightness properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1590–1602 (2007)Google Scholar
  4. 4.
    Biswas, A., Khara, S., Bhowmik, P., Bhattacharya, B.B.: Extraction of region of interest from face images using cellular analysis. ACM Compute 2008, 1–8 (2008)Google Scholar
  5. 5.
    Chai, H.Y., Wee, L.K., Swee, T.T., Salleh, S.H., Ariff, A.K., Kamarulafizam: Gray-level co-occurrence matrix bone fracture detection. American Journal of Applied Sciences, 26–32 (2011)Google Scholar
  6. 6.
    Donnelley, M., Knowles, G.: Automated bone fracture detection. In: Proceedings of SPIE 5747, Medical Imaging: Image Processing, p. 955 (2005)Google Scholar
  7. 7.
    Donnelley, M., Knowles, G., Hearn, T.: A CAD system for long-bone segmentation and fracture detection. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 153–162. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Eksi, Z., Dandil, E., Cakiroglu, M.: Computer-aided bone fracture detection. In: Proceedings of Signal Processing and Communications Applications, pp. 1–4 (2012)Google Scholar
  9. 9.
    Freeman, H.: On the encoding of arbitrary geometric configurations. IRE Trans. Electronic Computers, 260–268 (1961)Google Scholar
  10. 10.
    Hacihaliloglu, I., Abugharbieh, R., Hodgson, A.J., Rohling, R.N., Guy, P.: Automatic bone localization and fracture detection from volumetric ultrasound images using 3-d local phase features. Ultrasound Med. Biol. (1), 128–144 (2012)Google Scholar
  11. 11.
    Lum, V.L.F., Leow, W.K., Chen, Y.: Combining classifiers for bone fracture detection in X-ray images. In: IEEE International Congress on Image and Signal Processing, 1149–1152 (2005)Google Scholar
  12. 12.
    Materka, A., Cichy, P., Tuliszkiewicz, J.: Texture analysis of X-ray images for detection of changes in bone mass and structure. In: Texture Analysis in Machine Vision. p. 257, World Scientific (2000)Google Scholar
  13. 13.
    Muller, M.E., Nazarian, S., Koch, P., Schatzker, J.: The comprehensive classification of fractures of long bones. Springer (1990)Google Scholar
  14. 14.
    Ouyang, X., Majumdar, S., Link, T.M., Lu, Y., Augat, P., Lin, J., Newitt, D., Genant, H.K.: Morphometric texture analysis of spinal trabecular bone structure assessed using orthogonal radiographic projections. Medical Physics Research and Practice, 2037–2945 (1998)Google Scholar
  15. 15.
    Rosenfeld, A.: Digital straight line segments. IEEE Transactions on Computers, 1264–1269 (1974)Google Scholar
  16. 16.
    Tian, T.-P., Chen, Y., Leow, W.-K., Hsu, W., Howe, T.S., Png, M.A.: Computing neck-shaft angle of femur for X-ray fracture detection. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 82–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Wei, Z., Liming, Z.: Study on recognition of the fracture injure site based on X-ray images. In: IEEE International Congress on Image and Signal Processing, pp. 1947–1950 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Oishila Bandyopadhyay
    • 1
  • Arindam Biswas
    • 1
  • Bhargab B. Bhattacharya
    • 2
  1. 1.Department of Information TechnologyBengal Engineering and Science UniversityHowrahIndia
  2. 2.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

Personalised recommendations