Evaluation of image registration spatial accuracy using a Bayesian hierarchical model

Suyu Liu, Ying Yuan, Richard Castillo, Thomas Guerrero, Valen E. Johnson

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


To evaluate the utility of automated deformable image registration (DIR) algorithms, it is necessary to evaluate both the registration accuracy of the DIR algorithm itself, as well as the registration accuracy of the human readers from whom the "gold standard" is obtained. We propose a Bayesian hierarchical model to evaluate the spatial accuracy of human readers and automatic DIR methods based on multiple image registration data generated by human readers and automatic DIR methods. To fully account for the locations of landmarks in all images, we treat the true locations of landmarks as latent variables and impose a hierarchical structure on the magnitude of registration errors observed across image pairs. DIR registration errors are modeled using Gaussian processes with reference prior densities on prior parameters that determine the associated covariance matrices. We develop a Gibbs sampling algorithm to efficiently fit our models to high-dimensional data, and apply the proposed method to analyze an image dataset obtained from a 4D thoracic CT study.

Original languageEnglish (US)
Pages (from-to)366-377
Number of pages12
Issue number2
StatePublished - Jun 2014
Externally publishedYes


  • Bayesian analysis
  • Image processing
  • Latent variable
  • Spatial correlation

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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