Reliability-Driven, Spatially-Adaptive Regularization for Deformable Registration

  • Lisa Tang
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6204)

Abstract

We propose a reliability measure that identifies informative image cues useful for registration, and present a novel, data-driven approach to spatially adapt regularization to the local image content via use of the proposed measure. We illustrate the generality of this adaptive regularization approach within a powerful discrete optimization framework and present various ways to construct a spatially varying regularization weight based on the proposed measure. We evaluate our approach within the registration process using synthetic experiments and demonstrate its utility in real applications. As our results demonstrate, our approach yielded higher registration accuracy than non-adaptive approaches and the proposed reliability measure performed robustly even in the presences of noise and intensity inhomogenity.

Keywords

Coherence Lester 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lisa Tang
    • 1
  • Ghassan Hamarneh
    • 1
  • Rafeef Abugharbieh
    • 2
  1. 1.Medical Image Analysis Lab., School Computing ScienceSimon Fraser University 
  2. 2.Biomedical Signal and Image Computing Lab., Department of Electrical and Computer EngineeringUniversity of British Columbia 

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