Multicriteria 3D PET image segmentation

Francisco Javier Alvarez Padilla 1, 2 Eloïse Grossiord 3, 4 Barbara Romaniuk 1 Benoît Naegel 2 Camille Kurtz 5 Hugues Talbot 3 Laurent Najman 3 Romain Guillemot 1 Dimitri Papathanassiou 1, 6 Nicolas Passat 1
1 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804
2 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
3 LIGM - Laboratoire d'Informatique Gaspard-Monge
4 Keosys
5 LIPADE - EA 2517 - Laboratoire d'Informatique Paris Descartes
6 Institut Jean Godinot
Abstract : The analysis of images acquired with Positron Emission Tomography (PET) is challenging. In particular, there is no consensus on the best criterion to quantify the metabolic activity for lesion detection and segmentation purposes. Based on this consideration, we propose a versatile knowledge-based segmen-tation methodology for 3D PET imaging. In contrast to previous methods, an arbitrary number of quantitative criteria can be involved and the experts behaviour learned and reproduced in order to guide the segmentation process. The classification part of the scheme relies on example-based learning strategies, allowing interactive example definition and more generally incremental refinement. The image processing part relies on hierarchical segmentation, allowing vectorial attribute handling. Preliminary results on synthetic and real images confirm the relevance of this methodology, both as a segmentation approach and as an experimental framework for criteria evaluation.
Keywords : hierarchical segmentation Positron Emission Tomography knowledge-based system example-based learning
Type de document :
Communication dans un congrès
Image Processing Theory, Tools and Applications (IPTA), 2015, Orléans, France. 2016, 〈10.1109/IPTA.2015.7367162〉
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger
https://hal.archives-ouvertes.fr/hal-01616446
Contributeur : Laurent Najman <>
Soumis le : lundi 16 octobre 2017 - 21:11:08
Dernière modification le : mercredi 11 avril 2018 - 12:12:03
Document(s) archivé(s) le : mercredi 17 janvier 2018 - 12:19:11

Fichier

Alvarez Padilla IPTA 2015_VFin...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

LIGM | ENPC | PARISTECH | LIGM_A3SI | UPEC-UPEM | URCA | CRESTIC | FNCLCC | LIPADE | GODINOT | INC-CNRS | LRN | SITE-ALSACE

Citation

Francisco Javier Alvarez Padilla, Eloïse Grossiord, Barbara Romaniuk, Benoît Naegel, Camille Kurtz, et al.. Multicriteria 3D PET image segmentation. Image Processing Theory, Tools and Applications (IPTA), 2015, Orléans, France. 2016, 〈10.1109/IPTA.2015.7367162〉. 〈hal-01616446〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

272

Téléchargements de fichiers

34

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD