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Fractured Bone Identification from CT Images, Fragment Separation and Fracture Zone Detection

  • Félix Paulano
  • Juan J. Jiménez
  • Rubén Pulido
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 19)

Abstract

The automation of the detection of fractured bone tissue would allow to save time in medicine. In many cases, specialists need to manually revise 2D and 3D CT images and detect bone fragments and fracture regions in order to check a fracture. The identification of bone fragments from CT images allows to remove image noise and undesirable parts and thus improves image visualization. In addition, the utilization of models reconstructed from CT images of patients allows to customize the simulation, since the result of the identification can be used to perform a reconstruction that provides a 3D model of the patient anatomy. The detection of fracture zones increases the information provided to specialists and enables the simulation of some medical procedures, such as fracture reduction. In this paper, the main issues to be considered in order to identify bone tissue and the additional problems that arise if the bone is fractured are described. The identification of fractured bone includes not only bone tissue segmentation, but also bone fragments labelling and fracture region detection. Moreover, some fragments can appear together after the segmentation process, hence additional processing can be required to separate them. After that, currently proposed approaches to identify fractured bone are analysed and classified. The most recently proposed methods to segment healthy bone are also reviewed in order to justify that the techniques used for this type of bone are not always suitable for fractured bone. Finally, the aspects to be improved in the described methods are outlined and future work is identified.

Keywords

Fracture Zone Fracture Bone Fracture Line Bone Fragment Seed Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work has been partially supported by the Ministerio de Economía y Competitividad and the European Union (via ERDF funds) through the research project TIN2011-25259.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Félix Paulano
    • 1
  • Juan J. Jiménez
    • 1
  • Rubén Pulido
    • 1
  1. 1.University of JaénJaénSpain

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