Numerical Methods for Image Registration

By Jan Modersitzki. 2004

1,067 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3a05135cdd641295c38e91765472d46c1adb407c

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Intensity Gradient Based Registration and Fusion of Multi-modal Images (2006, background): “As described in [15], there are basically two registration approaches.”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “The reader is referred to [21] and [35] for an overview on nonparametric methods.”
Adaptive Stochastic Gradient Descent Optimisation for Image Registration (2009, methodology): “It would be interesting to integrate the ASGD method also in a nonparametric registration framework (Modersitzki 2004).”
Diffeomorphic demons: Efficient non-parametric image registration (2009, methodology): “Several teams (Bro-Nielsen and Gramkov, 1996; Cachier et al., 2003; Modersitzki, 2004; Pennec et al., 1999) have worked towards providing a theoretical framework for the demons in order to understand and potentially modify the underlying assumptions.”
Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach (2017, methodology): “Image registration is typically formulated as an optimization problem [1], optimizing the parameters of a transformation model.”
Fast Predictive Image Registration (2016, neutral): “Image registration is typically cast as an optimization problem, which can be especially computationally demanding for non-parametric diﬀusive, elastic, or ﬂuid models [10] such as LDDMM [1].”
Neurobiology of Aging: Contents (2005, neutral): “…Simoncini and Arioli 1999) • finance (Markowitz 1959, Markowitz and Perold 1981) • image reconstruction (Hall 1979) • image registration (Haber and Modersitzki 2004, Modersitzki 2003) • interpolation of scattered data (Lyche, Nilssen and Winther 2002, Sib- son and Stone 1991) • linear elasticity…”
Non-parametric Diffeomorphic Image Registration with the Demons Algorithm (2007, positive): “It has been shown in [7, 8] that the original demons algorithm could be seen as an optimization procedure on the entire space of displacement fields.”
Deformable medical image registration: setting the state of the art with discrete methods. (2011, background): “Egeo(T ) =Mgeo(K ◦ T,Λ) +Rgeo(T ) , (13)”
Deformable Medical Image Registration: A Survey (2013, methodology): “Following [5], currently employed physical models can be further separated in five categories (see Fig.”