An object-based volumetric deformable atlas for the improved localization of neuroanatomy in MR images

  • Tim McInerney
  • Ron Kikinis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


We present a hierarchical object-based deformable atlas, a promising new approach for the automatic localization and quantitative analysis of neuroanatomy in MR images. The 3D finite element-based elastic atlas combines the advantages of both volumetric-and surface-based deformable atlases in one single unifying framework. This multiresolution framework is not only capable of deforming entire volumes or subvolumes but can deform individual atlas objects, allowing greater and more effective use of object shape and local image feature information. Object surface representations are embedded in the volumetric deformable atlas and image-feature-derived forces acting on these surfaces are automatically transferred to the containing 3D finite element lattice. Consequently, spatial relationship constraints of the atlas objects are maintained via the elastic lattice while an object is deformed to match a target boundary. Atlas objects are deformed in a hierarchical fashion, begining with objects exhibiting well-defined image features in the target scan and proceeding to objects with slightly less well-defined features. Experiments involving several subcortical atlas objects are presented.


Hierarchical Fashion Neighboring Object Cubical Element Elastic Lattice Linear Elastic Body 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Tim McInerney
    • 1
    • 2
  • Ron Kikinis
    • 3
  1. 1.University of TorontoTorontoCanada
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Harvard Medical SchoolBostonUSA

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