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Computer Vision Based Hairline Mandibular Fracture Detection from Computed Tomography Images

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Advanced Computational Approaches to Biomedical Engineering

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Abstract

This chapter addresses the problem of detection of hairline mandibular fractures from a sequence of computed tomography (CT) images. It has been observed that such a fracture can be easily overlooked during manual detection due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT images. In this work, the 2D CT image slices of a mandible with hairline fractures are first identified from the input sequence of a fractured craniofacial skeleton. Two intensity-based image retrieval schemes with different measures of similarity, namely the Jaccard index and the Kolmogorov–Smirnov distance, are applied for that purpose. In the second part, we detect a hairline fracture in the previously identified subset of images using the maximum flow-minimum cut algorithm. Since a hairline fracture is essentially a discontinuity in the bone contour, we model it as a minimum cut in an appropriately weighted flow network constructed using the geometry of the human mandible. The Ford–Fulkerson algorithm with Edmonds–Karp refinement is employed to obtain a minimum cut. Experimental results demonstrate the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    Ananda S. Chowdhury

  2. Department of Computer Science, Dream Institute of Technology, Kolkata, 700104, India

    Anindita Mukherjee

  3. Department of Computer Science, University of Georgia, Augusta, GA, 30912, USA

    Suchendra M. Bhandarkar

  4. Department of Plastic Surgery, Georgia Health Sciences University, Athens, GA, 30602, USA

    Jack C. Yu

Authors
  1. Ananda S. Chowdhury
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  2. Anindita Mukherjee
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  3. Suchendra M. Bhandarkar
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  4. Jack C. Yu
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Corresponding author

Correspondence to Ananda S. Chowdhury .

Editor information

Editors and Affiliations

  1. Dept. of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA

    Punam K. Saha

  2. Dept. of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Ujjwal Maulik

  3. Dept. of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Subhadip Basu

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Chowdhury, A.S., Mukherjee, A., Bhandarkar, S.M., Yu, J.C. (2014). Computer Vision Based Hairline Mandibular Fracture Detection from Computed Tomography Images. In: Saha, P., Maulik, U., Basu, S. (eds) Advanced Computational Approaches to Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41539-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-41539-5_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41538-8

  • Online ISBN: 978-3-642-41539-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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