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Robust brain registration using adaptive probabilistic atlas.

Jaime Ide1, Rong Chen, Dinggang Shen

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. jaime.ide@uphs.upenn.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 6, 2008
PubMed
Summary

This study introduces a new framework for robust brain image registration, improving accuracy even with poor initial segmentation. The Adaptive Generalized Expectation Maximization (AGEM) method enhances segmentation and registration simultaneously.

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Computational Anatomy

Background:

  • Elastic image registration is crucial for aligning brain images to templates.
  • HAMMER is an accurate registration algorithm but sensitive to initial segmentation quality.
  • Poor segmentation quality negatively impacts registration performance.

Purpose of the Study:

  • To develop a novel framework enhancing HAMMER's robustness to imperfect initial segmentation.
  • To integrate segmentation and registration for improved accuracy and reliability.
  • To address limitations of traditional segmentation-dependent registration methods.

Main Methods:

  • Proposed a new framework using Adaptive Generalized Expectation Maximization (AGEM).
  • Employed an adaptive strategy incorporating spatial information from a probabilistic atlas.
  • Unified segmentation and registration into a single iterative process.

Main Results:

  • The integrated AGEM approach significantly improved registration accuracy.
  • The iterative method demonstrated robustness against low tissue contrast.
  • HAMMER's performance was enhanced despite initial segmentation challenges.

Conclusions:

  • The proposed AGEM framework offers a robust solution for elastic brain image registration.
  • Simultaneous segmentation and registration improve overall accuracy and reliability.
  • This method overcomes limitations posed by poor initial segmentation and low contrast in MR images.