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Geodesic regression for image time-series.

Marc Niethammer1, Yang Huang, François-Xavier Vialard

  • 1UNC Chapel Hill, USA.

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|October 15, 2011
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Summary
This summary is machine-generated.

This study introduces a novel generative model for image-time series registration, extending linear regression with a dynamic formulation. This approach offers a compact representation of spatio-temporal trajectories for efficient image analysis.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Current image-time series registration methods include pairwise concatenation, joint estimation with geodesic paths, local averaging, and temporal irregularity penalties.
  • These methods have limitations in efficiently representing the full spatio-temporal dynamics.

Purpose of the Study:

  • To propose a new generative model for image-time series registration.
  • To extend least squares linear regression to image spaces using a second-order dynamic formulation.

Main Methods:

  • A generative model is proposed, extending linear regression to image spaces.
  • A second-order dynamic formulation is employed for image registration.
  • The optimization problem is solved using an adjoint method.

Main Results:

  • The proposed formulation allows for a compact representation of the spatio-temporal trajectory approximation via initial values.
  • This method enables the design of image-based approximation algorithms.

Conclusions:

  • The novel generative model provides an efficient and compact approach to image-time series registration.
  • The method facilitates the development of new approximation algorithms for dynamic image analysis.