TY - GEN

T1 - Registration of unseen images based on the generative manifold modeling of variations of appearance and anatomical shape in brain population

AU - Zhang, Weiwei

AU - Davatzikos, Christos

PY - 2012/4/24

Y1 - 2012/4/24

N2 - In this paper we propose a method to register a pair of images unseen to the original dataset based on a generative manifold model. The basic premise of this approach is to design an image distance metric using a weighted sum of similarity and smoothness terms derived from a diffeomorphic registration of pairwise images. A refined image distance matrix based on this metric can be adopted as an input for nonlinear dimensionality reduction of the dataset, and the learned manifold can be approximated to simultaneously reflect the variations of appearance and anatomical shape. The generative manifold model that combines the image distance measurement and the manifold learning technique is used to estimate the geodesic path via the unseen pair for composition of the final deformation field. The experimental result of a set of real 3D mouse brain volumes demonstrates that the estimated manifold coordinates appropriately reflect the trend in the original dataset and that the registration of unseen images using the shortest path inferred from the generative manifold model improves the result against the direct registration.

AB - In this paper we propose a method to register a pair of images unseen to the original dataset based on a generative manifold model. The basic premise of this approach is to design an image distance metric using a weighted sum of similarity and smoothness terms derived from a diffeomorphic registration of pairwise images. A refined image distance matrix based on this metric can be adopted as an input for nonlinear dimensionality reduction of the dataset, and the learned manifold can be approximated to simultaneously reflect the variations of appearance and anatomical shape. The generative manifold model that combines the image distance measurement and the manifold learning technique is used to estimate the geodesic path via the unseen pair for composition of the final deformation field. The experimental result of a set of real 3D mouse brain volumes demonstrates that the estimated manifold coordinates appropriately reflect the trend in the original dataset and that the registration of unseen images using the shortest path inferred from the generative manifold model improves the result against the direct registration.

UR - http://www.scopus.com/inward/record.url?scp=84859911588&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859911588&partnerID=8YFLogxK

U2 - 10.1109/MMBIA.2012.6164737

DO - 10.1109/MMBIA.2012.6164737

M3 - Conference contribution

AN - SCOPUS:84859911588

SN - 9781467303521

T3 - Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

SP - 113

EP - 118

BT - 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012

T2 - 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012

Y2 - 9 January 2012 through 10 January 2012

ER -