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Grafip: A framework for the representation of healthy and pathological cerebral information

  • J. Atif
  • , C. Hudelot
  • , O. Nempont
  • , N. Richard
  • , B. Batrancourt
  • , E. Angelini
  • , I. Bloch
  • CNRS LTCI
  • Université des Antilles et de la Guyane
  • Ecole Centrale Paris
  • AP-HP

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This paper presents a contribution to the large problematic of integrating medical image-based information into a structured framework (such as electronic patient records or anatomofunctional databases). In neuroscience, the complexity of the cerebral anatomy, the wealth of information embedded in imaging data, as well as the difficulty of their interpretation, can benefit from the use of a structural brain model representing prior generic knowledge, which includes information on anatomical structures and their spatial relations. In this paper we describe a novel generic brain model, based on graph representations, and an instantiation procedure for individual patients, based on image segmentation. A complete patientspecific modeling framework is proposed that can be integrated into powerful computational tools to assist image data reviewing, diagnosis and therapeutic patient follow up.

Original languageEnglish
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
PublisherIEEE Computer Society
Pages205-208
Number of pages4
ISBN (Print)1424406722, 9781424406722
DOIs
Publication statusPublished - 1 Jan 2007
Externally publishedYes
Event4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007 - Arlington, VA, United States
Duration: 12 Apr 200715 Apr 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Conference

Conference4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007
Country/TerritoryUnited States
CityArlington, VA
Period12/04/0715/04/07

Keywords

  • Generic brain model
  • Graphs
  • Individual model
  • Knowledge representation

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