The adaptive bases algorithm for intensity-based nonrigid image registration

By Gustavo Kunde Rohde, Akram Aldroubi, B. Dawant. IEEE Transactions on Medical Imaging, 2003

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Longitudinal Changes in Cortical Thickness Associated with Normal Aging (2010, methodology): “We create R A (v) as follows: Given N(=17) atlases A 1 , A 2 , …, A N with labels L i (v) on each voxel v we obtain F A i (v) by deformably registering A i against each of A 1 , …, A N , (excluding A i ) using the Adaptive Bases Algorithm (ABA) (Rohde et al., 2003).”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “technique is one of the basic evaluation methods [215], [219], [220], [223], [233], [251].”
Deformable Medical Image Registration: A Survey (2013, methodology): “[125] applied the Wu functions in image registration and derived bounds for”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “several parametric nonrigid transformation models have been proposed [3], [28], [30], [32], [36], [37], each having its own advantages and disadvantages.”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “, 2008), relied on nonlinear deformation models, such as spline-based parameterized transformations (Rohde et al., 2003; Rueckert et al., 1999) or non-parametric diffeomorphisms (Beg et al.”
Optimization (2019, background): “These are most frequently used in medical image processing [21].”