Consistent landmark and intensity-based image registration

By Hans J. Johnson, Gary E. Christensen. IEEE Transactions on Medical Imaging, 2002

478 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fe5647c72c96d7acbd501aab689516b3efb6b766

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, unknown): “It is worth noting that other elastic registration algorithms can also be utilized, including but not limited to the methods proposed in (Ashburner and Friston, 1999; Christensen et al., 1996; Chui and Rangarajan, 2003; Collins et al., 1994; Gee et al., 1993; Johnson and Christensen, 2002; Luan et a”
Elastic registration of biological images using vector-spline regularization (2005, background): “To the best of our knowledge, the simultaneous use of all three terms is performed in [31], [32], [33].”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “Consistency tests have also been employed as a part of validation in several studies [77], [202], [212], [228].”
Deformable medical image registration: setting the state of the art with discrete methods. (2011, methodology): “We can either simply use the estimated transformation Tgeo as an initialization for the iconic formulation (54) as”
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain (2008, methodology): “As noted in Section 1, this method is quite distinct from inverse consistent image registration (ICIR) (Johnson and Christensen, 2002).”
A Review of Geometric Transformations for Nonrigid Body Registration (2008, methodology): “Christensen and Johnson [138], [139] used inverse consistency as regularization constraint.”
Non-rigid image registration: theory and practice. (2004, background): “Some work has been done on symmetric schemes which guarantee the same result whether image A is matched to image B or vice versa [26].”
Construction of a 3D probabilistic atlas of human cortical structures (2008, background): “... data from different subjects into a common coordinate frame; numerous research efforts have been made to meet these demands (e.g., Bajcsy et al., 1983; Gee et al., 1993; Woods et al., 1993, 1998; Collins et al., 1994; Davatzikos et al., 1996; Wells et al., 1996; Thompson and Toga, 1996; Christen”
Deformable Medical Image Registration: A Survey (2013, methodology): “Johnson and Christensen’s consistent registration approach [379] as well as Rueckert et al.”
A Review on Medical Image Registration as an Optimization Problem (2017, methodology): “The consistent image registration algorithm proposed by Johnson and Christensen [94] and the FFD registration algorithm proposed by Rueckert [95] are both GD methods of optimization.”
TransMorph: Transformer for unsupervised medical image registration (2021, unknown): “Isotropic diffusion (equivalent to Gaussian smoothing) (Balakrishnan et al., 2019), anisotropic diffusion (Pace et al., 2013), total variation (Vishnevskiy et al., 2016), and bending energy (Johnson and Christensen, 2002) are popular options for 𝑅 ( 𝜙 ) .”
A framework to study the cortical folding patterns. (2004, background): “In order to achieve reliable matching of some landmark sulci, additional constraints have to be added into the similarity measure (Thompson and Toga, 1996; Collins et al., 1998; Johnson and Christensen, 2002; Hellier and Barillot, 2003).”