Matrix computations (3rd ed.)
Bootstrap resampling for image registration uncertainty estimation without ground truth
IEEE Transactions on Image Processing
Summarizing and visualizing uncertainty in non-rigid registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Introduction to the non-rigid image registration evaluation project (NIREP)
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Performance bounds on image registration
IEEE Transactions on Signal Processing
Fast parametric elastic image registration
IEEE Transactions on Image Processing
Fundamental performance limits in image registration
IEEE Transactions on Image Processing
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For image registration to be applicable in a clinical setting, it is important to know the degree of uncertainty in the returned point-correspondences. In this paper, we propose a data-driven method that allows one to visualize and quantify the registration uncertainty through spatially adaptive confidence regions. The method applies to various parametric deformation models and to any choice of the similarity criterion. We adopt the B-spline model and the negative sum of squared differences for concreteness. At the heart of the proposed method is a novel shrinkage-based estimate of the distribution on deformation parameters. We present some empirical evaluations of the method in 2-D using images of the lung and liver, and the method generalizes to 3-D.