Medical image registration

By Derek L. G. Hill, P. Batchelor, Mark Holden, D. Hawkes. Physics in Medicine and Biology, 2001

1,562 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/97da8f1683f140d14407f254d5d5e6aecdf0c3ad

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, neutral): “It aims at determining the spatial alignment between images of the same or different subjects by means of optimizing a cost function, which measures the similarity between the transformed input image and the reference image (template) [315] .”
Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach (2017, background): “x). Here, ddenotes the spatial dimension and xis the spatial coordinate of the xed target image T.Due to the importance of image registration, a large number of dierent approaches have been proposed [1,37,38,39]. Typically, these approaches are formulated as optimization problems, where one seeks to”
Weakly-supervised convolutional neural networks for multimodal image registration (2018, background): “Classical pairwise intensity-based image registration methods are in general based on optimising image similarity, a metric indicating how well image intensities correspond (Hill et al., 2001).”
Survey of Medical Image Registration (2013, methodology): “Clearly most of the human body does not conform to a rigid or even an affine approximation [1] and much of the most interesting work in registration today involves the development of non-rigid registration techniques for applications ranging from modeling, tissue deformations to variability in anato”
A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations (2001, background): “Even though there is a large number of registration methods to be found in the literature [2,3], existing methods have usually been designed for or fine-tuned to specific, mostly single-modality applications.”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “The image geometry correction was the 3D gradwarp correction (Hajnal et al., 2001; Jovicich et al. 2006). The B1 non-uniformity correction is detailed inNarayana et al. (1988). These twopreprocessing steps can be performed directly on the MRI console and thus seem feasible in clinical routine.”
Dense image registration through MRFs and efficient linear programming (2008, background): “Our framework currently contains a range of well-known similarity measures, namely the sum of absolute differences (SAD), the sum of squared differences (SSD), the normalized cross correlation (NCC) (Hajnal et al., 2001), the normalized mutual information (NMI) (Maes et al.”
Motion Correction Algorithms May Create Spurious Brain Activations in the Absence of Subject Motion (2001, methodology): “While this choice allows rapid minimization of the measure iteratively using a singular value decomposition (SVD), it may explain some differences with other implementations of the same similarity measure (Hill et al., 2001).”