Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees

By Torsten Rohlfing, Calvin R. Maurer Jr.. IEEE Transactions on Information Technology in Biomedicine, 2003

367 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1b0d7226b67c9ef3f1bdbc041134ed03d3c250c6

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Comprehensive Maps of Drosophila Higher Olfactory Centers: Spatially Segregated Fruit and Pheromone Representation (2007, positive): “We used a fully automatic, nonrigid, intensity-based 3D image registration algorithm (Rohlfing and Maurer, 2003).”
Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint (2003, methodology): “The first property allows for efficient computation of the transformation [33], while the second property allows for the analytical computation of continuous first- and second-order derivatives.”
Toward automated segmentation of the pathological lung in CT (2005, background): “Finally, it warrants mentioning that elastic registrations lend themselves well to parallel implementation [31].”
Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains (2004, methodology): “Using 48 processors on a 128-processor SGI Origin 3800 supercomputer (MIPS R12K processors running at 400 MHz), the computation time per registration is about 1 min (Rohlfing and Maurer, 2003).”
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable (2011, methodology): “2) Our own implementation [12] (available as part of CMTK [8]) of Rueckert’s free-form deformation (FFD) registration algorithm [13] based on a cubic B-spline transformation model.”
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease (2013, background): “Rohlﬁng and Maurer (2003) proposed a scheme for nonrigid registration using ﬁnite differences for the derivative computation, distributing the elements of the derivative over the processing elements.”
MRI Estimates of Brain Iron Concentration in Normal Aging Using Quantitative Susceptibility Mapping (2011, methodology): “Image registration As previously described (Pfefferbaum et al., 2009), for each subject and for 1.5T and 3.0T separately, the late-echo EPSE data were nonrigidly registered (Rohlfing and Maurer, 2003) [http://nitrc.org/projects/cmtk/] to the early-echo EPSE data.”
Whole-brain activity mapping onto a zebrafish brain atlas (2015, methodology): “Registration quality is accurate to ~1 cell body diameter and has been validated across species 25–27 , different microscopes, and even imaging modalities; we have successfully registered 2-photon imaging data into the Z-Brain (Fig.”
Fast free-form deformation using graphics processing units (2010, background): “Furthermore, as explained in Rohlfing et al.[5], this approach allowed precomputation of the cubic B-spline basis values for each node, thus easing the calculation further.”