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Volume image registration by cross-entropy optimization.

Yang-Ming Zhu1

  • 1Nuclear Medicine Division, Philips Medical Systems (formerly Marconi Medical Systems), Cleveland, OH 44143, USA. yzhu@computer.org

IEEE Transactions on Medical Imaging
|April 4, 2002
PubMed
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This study introduces cross-entropy (CE) for volume image registration. Minimizing or maximizing CE, depending on prior knowledge, optimizes image alignment for better accuracy and robustness.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Information Theory

Background:

  • Volume image registration aligns 3D medical images.
  • Information-theoretic measures are crucial for registration accuracy.
  • Cross-entropy (CE) quantifies differences between probability distributions.

Purpose of the Study:

  • To apply cross-entropy (CE) as a criterion for volume image registration.
  • To explore CE minimization and maximization strategies based on prior distribution knowledge.
  • To develop a novel registration approach by combining different CE formulations.

Main Methods:

  • Formulated image registration criteria using cross-entropy (CE).
  • Investigated CE minimization for known joint distributions.

Related Experiment Videos

  • Explored CE maximization, reducing to joint entropy, conditional entropy, and mutual information minimization under specific unlikely distributions.
  • Combined various cross-entropy measures as registration criteria.
  • Tested accuracy and robustness using likely and unlikely joint distributions.

    • Main Results:

    • Demonstrated the application of cross-entropy (CE) for optimizing volume image registration.
    • Showcased how CE maximization relates to joint entropy, conditional entropy, and mutual information.
    • Validated the accuracy and robustness of the proposed combined CE approach.

    Conclusions:

    • Cross-entropy (CE) provides a flexible and powerful framework for volume image registration.
    • The combined CE approach offers robust and accurate image alignment.
    • This method enhances image registration by leveraging information-theoretic principles.