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DTI Connectivity by Segmentation

  • Marc Niethammer
  • Alexis Boucharin
  • Christopher Zach
  • Yundi Shi
  • Eric Maltbie
  • Mar Sanchez
  • Martin Styner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)

Abstract

This paper proposes a new method to compute connectivity information from diffusion weighted images. It is inspired by graph-based approaches to connectivity definition, but formulates the estimation problem in the continuum. In particular, it defines the connectivity through the minimum cut in tensor-weighted space. It is therefore closely related to prior work on segmentation using continuous versions of graph cuts. A numerical solution based on a staggered grid is proposed which allows for the computation of flux directly through diffusion tensors. The resulting global connectivity measure is the maximum diffusive flow supported between two regions of interest.

Keywords

Stagger Grid Tensor Model Connectivity Measure Connectivity Information Length Bias 
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 2010

Authors and Affiliations

  • Marc Niethammer
    • 1
  • Alexis Boucharin
    • 1
  • Christopher Zach
    • 2
  • Yundi Shi
    • 1
  • Eric Maltbie
    • 1
  • Mar Sanchez
    • 3
  • Martin Styner
    • 1
  1. 1.University of North Carolina at Chapel Hill 
  2. 2.Swiss Federal Institute of Technology (ETH)Zurich
  3. 3.Emory University 

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