Skip to Main content Skip to Navigation
Conference papers

Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape prior

Timothée Evain 1, 2 Xavier Ripoche J. Atif Isabelle Bloch 1, 2
1 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
LTCI - Laboratoire Traitement et Communication de l'Information
2 IDS - Département Images, Données, Signal
Abstract : We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images for which a reference segmentation was available, with an overall Dice coefficient above 0.95 and a global consistency error less than 0.005.
Document type :
Conference papers
Complete list of metadatas
https://hal.telecom-paris.fr/hal-02288484
Contributor : Telecomparis Hal <>
Submitted on : Saturday, September 14, 2019 - 6:52:37 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:54 PM

Identifiers

  • HAL Id : hal-02288484, version 1

Collections

LTCI | PARISTECH | INSTITUT-TELECOM | IDS | IMAGES | UNIV-PARIS-SACLAY

Citation

Timothée Evain, Xavier Ripoche, J. Atif, Isabelle Bloch. Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape prior. IEEE International Symposium on Biomedical Imaging, 2017, Melbourne, Australia. pp.1197-1200. ⟨hal-02288484⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

31

Contact

Informations

CCSD