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Automatic Intracranial Space Segmentation for Computed Tomography Brain Images

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Abstract

Craniofacial disorders are routinely diagnosed using computed tomography imaging. Corrective surgery is often performed early in life to restore the skull to a more normal shape. In order to quantitatively assess the shape change due to surgery, we present an automated method for intracranial space segmentation. The method utilizes a two-stage approach which firstly initializes the segmentation with a cascade of mathematical morphology operations. This segmentation is then refined with a level-set-based approach that ensures that low-contrast boundaries, where bone is absent, are completed smoothly. We demonstrate this method on a dataset of 43 images and show that the method produces consistent and accurate results.

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Authors and Affiliations

  1. Developmental Imaging, Murdoch Childrens Research Institute, Parkville, Melbourne, Australia

    C. Adamson, R. Beare & A. G. Wood

  2. Department of Plastic and Maxillofacial Surgery, The Royal Children’s Hospital, Melbourne, Australia

    A. C. Da Costa

  3. Australian Centre for Child Neuropsychology Studies, Murdoch Childrens Research Institute, Melbourne, Australia

    A. C. Da Costa & A. G. Wood

  4. Department of Medicine, Southern Clinical School, Monash University, Melbourne, Australia

    R. Beare & A. G. Wood

  5. School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    A. G. Wood

Authors
  1. C. Adamson
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  2. A. C. Da Costa
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  3. R. Beare
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  4. A. G. Wood
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Corresponding author

Correspondence to A. G. Wood.

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Adamson, C., Da Costa, A.C., Beare, R. et al. Automatic Intracranial Space Segmentation for Computed Tomography Brain Images. J Digit Imaging 26, 563–571 (2013). https://doi.org/10.1007/s10278-012-9529-8

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  • DOI: https://doi.org/10.1007/s10278-012-9529-8

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