Non-rigid image registration: theory and practice.
By William R. Crum, Thomas Hartkens, Derek L. G. Hill. British Journal of Radiology, 2004
831 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/94512740e3bd7318b79ac1d126593f2684e71858
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Nikos Paragios
- Aristeidis Sotiras
- Christos Davatzikos
- Sébastien Ourselin
- Joseph V. Hajnal
- Latha Srinivasan
- Derek L. G. Hill
- Oscar Camara
In Radiology, Nuclear Medicine and Imaging
- Nikos Paragios
- Serena J. Counsell
- Sébastien Ourselin
- Latha Srinivasan
- Derek L. G. Hill
- Oscar Camara
- Mary A. Rutherford
- William R. Crum
Citations in the corpus
- Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013, background): “Readers are referred to [18]–[20] for surveys of such techniques.”
- Optimization (2019, neutral): “We have studied some of the state-of-the-art “nonrigid image registra-tion” techniques from the field of medical imaging [22].”
- Medical image registration: a review (2014, neutral): “Additionally,astudyongeometrictransform-ationsfornon-rigidimageregistrationcanbefoundin Crumetal.”
- Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants (2007, methodology): “VBM uses image registration to bring brain images from different subjects into a common coordinate system for analysis (Ashburner and Friston, 2000; Crum et al., 2004), and has been used in DTI studies of schizophrenia (Giuliani et al., 2005), white matter asymmetry (Buchel et al., 2004) and…”
- DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (2009, background): “Image registration is the process of finding the optimal transformation that aligns different imaging data into spatial correspondence, so that after registration, the same anatomic structures occupy the same spatial locations in different images (Zitova and Flusser, 2003; Crum et al., 2004).”
- Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (2015, neutral): “individual subjects to the reference space [1]–[5].”
- Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration (2013, neutral): “Several nonrigid registration algorithms have been reported in the literature [47]–[56].”
- Fast free-form deformation using graphics processing units (2010, background): “While a huge amount of research has been devoted to the methodological development [1] [2], very little research has focused on the computational burden of the proposed algorithms.”
- Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis (2006, background): “Registration refers to a set of techniques with the shared goal of establishing anatomical or functional correspondence between images acquired at different times, of different subjects, or with different modalities [1], [2].”