Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition: application to 3D brain imaging

  • Authors:

  • Isabelle Bloch;Thierry Géraud;Henri Maître

  • Affiliations:

  • Ecole Nationale Supérieure des Télécommunications, Département TSI - CNRS URA 820, 46 rue Barrault, 75013 Paris, France;Ecole Nationale Supérieure des Télécommunications, Département TSI - CNRS URA 820, 46 rue Barrault, 75013 Paris, France;Ecole Nationale Supérieure des Télécommunications, Département TSI - CNRS URA 820, 46 rue Barrault, 75013 Paris, France

  • Venue:
  • Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a novel approach to model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs segmentation and recognition of the scene simultaneously. The solution of the recognition task is progressive, processing successively different objects, and using different pieces of knowledge about the object and about relationships between objects. Therefore, the core of the approach is the knowledge representation part, and constitutes the main contribution of this paper. We make use of a spatial representation of each piece of information, as a spatial fuzzy set representing a constraint to be satisfied by the searched object, thanks in particular to fuzzy mathematical morphology operations. Fusion of these constraints allows us to select, segment and recognize the desired object.