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Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization

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Abstract

Recent clinical research studies in forensic identification have highlighted the interest in sphenoid sinus anatomical characterization. Their pneumatization, well known as extremely variable in degrees and directions, could contribute to the radiologic identification, especially if dental records, fingerPrints, or DNA samples are not available. In this paper, we present a new approach for automatic person identification based on sphenoid sinus features extracted from computed tomography (CT) images of the skull. First, we present a new approach for fully automatic 3D reconstruction of the sphenoid hemisinuses which combines the fuzzy c-means method and mathematical morphology operations to detect and segment the object of interest. Second, deep shape features are extracted from both hemisinuses using a dilated residual version of a stacked convolutional auto-encoder. The obtained binary segmentation masks are thus hierarchically mapped into a compact and low-dimensional space preserving their semantic similarity. We finally employ the ℓ2 distance to recognize the sphenoid sinus and therefore identify the person. This novel sphenoid sinus recognition method obtained 100% of identification accuracy when applied on a dataset composed of 85 CT scans stemming from 72 individuals.

Automatic Forensic Identification using 3D Sphenoid Sinus Segmentation and Deep Characterization from Dilated Residual Auto-Encoders

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Authors and Affiliations

  1. Medical Computing Laboratory (LIMED), University of Abderrahmane Mira, 06000, Bejaia, Algeria

    Kamal Souadih & Ahror Belaid

  2. Laboratory of Medical Information Processing (LaTIM), UMR 1101, Inserm, 22 avenue Camille Desmoulins, 29238, Brest, France

    Douraied Ben Salem & Pierre-Henri Conze

  3. Neuroradiology Department, CHRU la cavale blanche, Boulevard Tanguy Prigent, UBO, 29609, Brest, France

    Douraied Ben Salem

  4. IMT Atlantique, Technopôle Brest Iroise, 29238, Brest, France

    Pierre-Henri Conze

Authors
  1. Kamal Souadih
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  2. Ahror Belaid
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  3. Douraied Ben Salem
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  4. Pierre-Henri Conze
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Correspondence to Kamal Souadih.

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Souadih, K., Belaid, A., Ben Salem, D. et al. Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization. Med Biol Eng Comput 58, 291–306 (2020). https://doi.org/10.1007/s11517-019-02050-6

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  • DOI: https://doi.org/10.1007/s11517-019-02050-6

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