Symmetric image registration with directly calculated inverse deformation field

This paper presents a novel technique for a symmetric deformable image registration based on a new method for fast and accurate direct inversion of a large motion model deformation field. The proposed image registration algorithm maintain a one-to-one mapping between registered images by symmetrically warping them to each other, and by ensuring the inverse consistency criterion at each iteration. This makes the final estimation of forward and backward deformation fields anatomically plausible. The quantitative validation of the method has been performed on magnetic resonance data obtained for a pelvis area demonstrating applicability of the method to adaptive prostate radiotherapy. The experiments demonstrate the improved robustness in terms of inverse consistency error when compared to previously proposed methods for symmetric image registration

Direct inverse deformation field approach to pelvic-area symmetric image registration

This paper presents a novel technique for a consistent symmetric deformable image registration based on an accurate method for a direct inversion of a large motion model deformation field. The proposed image registration algorithm maintains one-to-one mapping between registered images by symmetrically warping them to another image. This makes the final estimation of forward and backward deformation fields anatomically plausible and applicable to adaptive prostate radiotherapy. The quantitative validation of the method is performed on magnetic resonance data obtained for pelvis area. The experiments demonstrate the improved robustness in terms of inverse consistency error and estimation accuracy of prostate position in comparison to the previously proposed methods

Facial Expression Recognition Using Log-Euclidean Statistical Shape Models

This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in Log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields, and deformation fields. The obtained results show that the facial expression representation based on stationary velocity field can be successfully utilised in facial expression recognition, and this parameterisation produces higher recognition rate than the facial expression representation based on deformation fields

Facial Expression Recognition using Diffeomorphic Image Registration Framework

This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in the log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields and deformation fields. The obtained results show that the facial expression representation based on stationary velocity fields can be successfully utilised in facial expression recognition, and this parameterisation produces a higher recognition rate than the facial expression representation based on deformation fields