A Review of Geometric Transformations for Nonrigid Body Registration

By Mark Holden. IEEE Transactions on Medical Imaging, 2008

444 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1be248f601ea0df83035b1e82c863ea20a6dd026

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Enzo Ferrante
2. João Manuel Ribeiro da Silva Tavares
3. Francisco P. M. Oliveira
4. Thomas Martin Deserno
5. Russell A. Poldrack
6. Yonghuai Liu
7. A. David Marshall
8. Nikos Paragios

In Computer Vision and Pattern Recognition

1. Thomas Martin Deserno
2. Benjamin Berkels
3. András P. Keszei
4. Enzo Ferrante
5. João Manuel Ribeiro da Silva Tavares
6. Francisco P. M. Oliveira
7. Russell A. Poldrack
8. Yonghuai Liu

Citations in the corpus

• Deformable Medical Image Registration: A Survey (2013, unknown): “The rest of the section is organized by following coarsely and extending the classification of deformation models given by Holden [14].”
• Handbook of Functional MRI Data Analysis: Index (2011, methodology): “…to deform brains to match one another without regard to the nature of the material being warped, computational anatomy methods generally use models based on physical phenomena, such as the deformation of elastic materials or the flow of viscous fluids (Christensen et al., 1994; Holden, 2008).”
• Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “Holden has reviewed non-rigid geometric transformation models [19].”
• A Review on Medical Image Registration as an Optimization Problem (2017, background): “Linear elasticity transformation is predominantly based on stress and strain theory, in which a contact force is applied to a given position in an orthogonal plane, and stress is used to confirm deformation in the image [23].”
• Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013, background): “We further discuss the problem of over-fitting and provides up-to-date references beyond those in existing surveys.”
• Medical image registration: a review (2014, background): “TPS is an interpolation function that minimises the bending energy (Holden 2008).”
• Slice-to-volume medical image registration: a survey (2017, background): “These deformations are particularly observed between CT and US exams in different respiratory configurations, and could be handled with affine transformations.”