Title
Geometrical regularization of nonrigid registration using local anisotropic structure and joint saliency map
Abstract
Nonrigid image registration is a crucial task to study local structural/volumetric change in many applications. The presence and resection of brain tumor in pre- and intra-operative brain images will greatly distort local anatomical structure and introduce non-corresponding outlier features. This can cause serious conflicts in achieving a smoothly varying deformation field in nonrigid registration. In this paper, a novel regularizing scheme, which is based on local anisotropic structure and Joint Saliency Map weighted regularization, is introduced in registration to aim at handling local complex deformation and outliers. The sparse displacement is regularized to adapt its smoothness as well as orientation according to the local anisotropic structure. Moreover, the Joint Saliency Map guides the assignment of data certainty so that the reliable corresponding structural voxels are emphasized in regularization. The results show that our method is sufficiently accurate and effective to both local large deformation and outliers while maintaining an overall smooth deformation field.
Year
DOI
Venue
2011
10.1117/12.896137
Proceedings of SPIE
Keywords
Field
DocType
Geometrical regularization,joint saliency map,normalized convolution,anisotropic applicability function
Voxel,Computer vision,Saliency map,Anisotropy,Outlier,Regularization (mathematics),Artificial intelligence,Deformation (mechanics),Smoothness,Mathematics,Image registration
Conference
Volume
Issue
ISSN
8009
null
0277-786X
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Jiawei Zhou150.74
Binjie Qin2507.85