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Evaluation of image registration spatial accuracy using a Bayesian hierarchical model
Author(s) -
Liu Suyu,
Yuan Ying,
Castillo Richard,
Guerrero Thomas,
Johnson Valen E.
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12146
Subject(s) - bayesian probability , computer science , image registration , artificial intelligence , hierarchical database model , bayesian hierarchical modeling , pattern recognition (psychology) , image (mathematics) , bayesian inference , data mining
Summary 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.