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Privacy preserving image registration.

Riccardo Taiello1, Melek Önen2, Francesco Capano2

  • 1Epione Research Group, Inria, Sophia Antipolis, France; EURECOM, France; Université Côte d'Azur, France.

Medical Image Analysis
|March 12, 2024
PubMed
Summary

This study introduces privacy-preserving image registration (PPIR) for confidential medical images. PPIR uses advanced cryptography to enable accurate image alignment without revealing sensitive patient data.

Keywords:
Image registrationTrustworthiness

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Cryptography

Background:

  • Image registration aligns medical images but requires unencrypted data.
  • Medical image analysis often needs privacy due to sensitive patient information.
  • Current methods fail when data cannot be shared openly.

Purpose of the Study:

  • To develop a privacy-preserving image registration framework.
  • To enable medical image analysis under strict confidentiality constraints.
  • To adapt classical registration methods for secure computation.

Main Methods:

  • Utilized secure multi-party computation and homomorphic encryption.
  • Optimized cryptographic operations using gradient approximations and packing techniques.
  • Applied the framework to rigid, affine, and non-linear registration tasks.

Main Results:

  • Demonstrated the feasibility of privacy-preserving operations for image registration.
  • Showcased effective privacy-preserving image registration (PPIR) across various complexity levels.
  • Validated the framework's performance and scalability for high-dimensional data.

Conclusions:

  • Privacy-preserving image registration is achievable using advanced cryptographic tools.
  • The proposed PPIR framework effectively handles confidential medical images.
  • This work enables secure medical image analysis while protecting patient privacy.