A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations

By Julia Anne Schnabel, Daniel Rueckert, M. Quist, J. Blackall, A. D. Castellano-Smith, Thomas Hartkens, G. Penney, W. Hall, Haiying Liu, C. Truwit and 3 more. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2001

441 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b1a3ba152c3b95ebe95afb33c6675637e872f8fd

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

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Optical flow modeling and computation: A survey (2015, background): “Image-adaptive non-regular control points distribution [212] or coarse-to-fine spacing strategies [211] are possibilities to address this issue.”
Dense image registration through MRFs and efficient linear programming (2008, methodology): “[31] propose a non-rigid image registration method 5 based on B-Spline FFD together with a gradient-descent optimization.”
Multi-Modal Neuroimaging Feature Learning for Multi-Class Diagnosis of Alzheimer’s Disease (2014, methodology): “All raw MR images were corrected following the ADNI MR image protocol and were nonlinearly registered to the ICBM_152 template [47] using the Image Registration Toolkit [48].”
Mutual-information-based registration of medical images: a survey (2003, background): “, [29], [30], [36], [74], [75], [82], [125], [127]).”
Medical image registration: a review (2014, methodology): “Lately, in Schnabel et al. (2001), a new framework was proposed by extending and generalising the technique previously presented in Rueckert et al. (1999).”
Deformable Medical Image Registration: A Survey (2013, methodology): “[140] proposed to usemultilevel B-splines.”
Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “The tool supports rigid, affine [70], and non-rigid transformations methods [65, 71].”
Validation of nonrigid image registration using finite-element methods: application to breast MR images (2003, background): “[15] and our recent extensions [42] with respect to local volume conservation and intrinsic registration parameters such as the choice of the control point mesh resolution.”
A deep learning framework for unsupervised affine and deformable image registration (2018, background): “Carefully chosen coarse-to-fine levels of multi-stage B-spline registration might prevent image folding and result in smooth deformations (Schnabel et al., 2001).”
A Viscous Fluid Model for Multimodal Non-rigid Image Registration Using Mutual Information (2002, methodology): “…reference frame asT5 x2 u(x), mapping fixedvoxel level detail, implying high computational complexity ¢voxel positions x in & onto the corresponding pointsunless a strategy for local adaptive grid refinement is used ¢ ¢ ¢x2 u(x) in the original templatê (Christensen et al.,(Schnabel et al., 2001).”
Early diagnosis of Alzheimer's disease with deep learning (2014, methodology): “All the MRI images are nonlinearly registered to the ICBM_152 template [8, 22, 23] and further segmented into 83 functional regions.”
The adaptive bases algorithm for intensity-based nonrigid image registration (2003, methodology): “This approach is similar to the methods investigated in [ 20 ] and [21].”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, background): “computational cost and to improve the local resolution of nonrigid transformations, several researchers have proposed adaptive techniques for the selection of control points in FFD [214], [243], [244] and also in NSCM [245] transformation”
Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling (2002, methodology): “To cope with large shape variations, a multiresolution extension [13] of the free-form registration algorithm proposed by Rueckert et al.”
Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration (2003, methodology): “We are using a nonrigid registration algorithm which has been previously applied successfully to a number of different registration tasks [33], [36].”
Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes (2018, methodology): “Closed surface triangulations of these segmentations were generated using the IRTK mcubes algorithm [17].”
Toward automated segmentation of the pathological lung in CT (2005, methodology): “Methods to speed up the process drastically are already available, e.g., by utilizing nonuniform grids of (therefore, less) control points [ 22 ], or by developing an implementation dedicated to the registration of lungs specifically.”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, unknown): “The non-rigid registration was carried out in a coarse-to-fine manner (Schnabel et al., 2001).”
Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint (2003, methodology): “Schnabel et al. [39], [45] have recently reported promising results using a simulation of deformations based on biomechanical modeling of tissue properties to validate nonrigid registration methods.”