Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization.
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,...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 2; pp. 291 - 307 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141513919&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141513919 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2020 vid: 58 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141513919 141513919 NLM31848978 10.1007/s11517-019-02050-6 NLM31848978 141513919 ppf: 291 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization. aug: au: Souadih, Kamal Belaid, Ahror Ben Salem, Douraied Conze, Pierre-Henri affil: Medical Computing Laboratory (LIMED), University of Abderrahmane Mira, 06000, Bejaia, Algeria sug: subj: Imaging, Three-Dimensional Sphenoid Sinus Forensic Medicine Automation Logic Algorithms Tomography, X-Ray Computed Scales ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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