Advertisement

A MRF Based Random Graph Modelling the Human Cortical Topography

  • J.-F. Mangin
  • J. Regis
  • I. Bloch
  • V. Frouin
  • Y. Samson
  • J. Lopez-Krahe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)

Abstract

This paper presents a project aiming at the automatic detection and recognition of the human cortical sulci in a 3D magnetic resonance image. The two first steps of this project (automatic extraction of an attributed relational graph (ARG) representing the individual cortical topography, constitution of a database of labelled ARGs) are briefly described. Then, a probabilistic structural model of the cortical topography is inferred from the database. This model, which is a structural prototype whose nodes can split into pieces according to syntactic constraints, relies on several original interpretations of the inter-individual structural variability of the cortical topography. This prototype is endowed with a random graph structure taking into account this anatomical variability. The recognition process is formalized as a labelling problem whose solution, defined as the maximum a posteriori estimate of a Markovian random field (MRF), is obtained using simulated annealing.

Keywords

Random Graph Markovian Random Field Semantic Attribute Structural Prototype Syntactic Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    V. Frouin, J.-F. Mangin, J. Regis, and B. Bendriem. A 3D editor of the cortical sulcal topography. In 7th. IEEE Comp. Based. Med. Syst., Winston Salem, 1994.Google Scholar
  2. 2.
    J.-F. Mangin, V. Frouin, I. Bloch, J. Regis, and J. Lopez-Krahe. Automatic construction of an attributed relational graph representing the cortex topography using homotopic transformations. In SPIE Mathematical Methods in Medical Imaging III, San Diego, vol. 2299, pages 110–121, July 1994.Google Scholar
  3. 3.
    J.-F. Mangin, J. Regis, I. Bloch, V. Frouin, Y. Samson, and J. Lopez-Krahe. Modélisation structurelle de la topographie corticale: un graphe aléatoire fondé sur un champ markovien. Technical Report D016, Télécom Paris, 1994.Google Scholar
  4. 4.
    M. Ono, S. Kubik, and C. D. Abernethey. Atlas of the Cerebral Sulci. Georg Thieme Verlag, 1990.Google Scholar
  5. 5.
    J. Regis. Deep sulcal anatomy and functional mapping of the cerebral cortex (in french). MD Thesis, Université d’Aix-Marseille I I, 1994.Google Scholar
  6. 6.
    J. Talairach and P. Tournoux. Co—Planar Stereotaaic Atlas of the Human Brain. 3—Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme Medical Publisher, Inc., Georg Thieme Verlag, Stuttgart, New York, 1988.Google Scholar
  7. 7.
    A. K. C. Wong and M. L. You. Entropy and distance of random graph with application to structural pattern recognition. IEEE PAMI, 7: 599–609, 1985.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • J.-F. Mangin
    • 1
    • 2
  • J. Regis
    • 3
  • I. Bloch
    • 1
  • V. Frouin
    • 2
  • Y. Samson
    • 2
  • J. Lopez-Krahe
    • 1
  1. 1.Département ImagesTélécom ParisParis Cedex 13France
  2. 2.Service Hospitalier Frédéric Joliot, Commissariat à l’Énergie AtomiqueOrsayFrance
  3. 3.Serv. de Neurochirurgie Fonctionnelle et StéréotaxiqueCHU La TimoneMarseilleFrance

Personalised recommendations