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Journal of Mathematical Imaging and Vision

, Volume 55, Issue 2, pp 179–198 | Cite as

A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

  • Carl LedermanEmail author
  • Anand Joshi
  • Ivo Dinov
  • John Darrell Van Horn
  • Luminita Vese
  • Arthur Toga
Article

Abstract

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.

Keywords

Image registration Active surfaces Finite elements 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Carl Lederman
    • 1
    Email author
  • Anand Joshi
    • 2
  • Ivo Dinov
    • 3
    • 4
  • John Darrell Van Horn
    • 2
  • Luminita Vese
    • 1
  • Arthur Toga
    • 2
  1. 1.Department of MathematicsUniversity of California Los AngelesLos AngelesUSA
  2. 2.Institute for Data Science, School of NursingUniversity of MichiganAnn ArborUSA
  3. 3.Statistics Online Computational Resource, Michigan Institute for Data Science, School of NursingUniversity of MichiganAnn ArborUSA
  4. 4.Department of Statistics, Center for Computational BiologyUniversity of California, Los Angeles (UCLA)Los AngelesUSA

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