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.
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