Interpolation revisited [medical images application]

By Philippe Thévenaz, Thierry Blu, Michael A. Unser. IEEE Transactions on Medical Imaging, 2000

837 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/60c18db93e722a90d8b5b20e247bbaf8479e95f0

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

1. Bruce Fischl
2. João Manuel Ribeiro da Silva Tavares
3. Francisco P. M. Oliveira
4. John Ashburner
5. Martin Reuter
6. Nicholas Ayache
7. Mert Rory Sabuncu
8. Xavier Pennec

In Signal Processing

1. John Ashburner
2. Nicholas Ayache
3. Xavier Pennec
4. Pierre Fillard
5. Michael A. Unser
6. Philippe Thévenaz
7. João Manuel Ribeiro da Silva Tavares
8. Francisco P. M. Oliveira

Citations in the corpus

• Within-subject template estimation for unbiased longitudinal image analysis (2012, methodology): “For this paper, we employ linear interpolation , but recently switched to cubic B-spline interpolation for future releases to reduce interpolation artifacts (Thevenaz et al., 2000).”
• A Riemannian Framework for Tensor Computing (2005, methodology): “Other popular methods include the cubic and, more generally, spline interpolations [Th´evenaz et al., 2000, Meijering, 2002].”
• Medical image registration: a review (2014, background): “A study on image interpolation function can be found in The´venaz et al. (2000).”
• User‐friendly semiautomated assembly of accurate image mosaics in microscopy (2007, methodology): “The (K 1 3 K 2 ) cubic-spline coefficients c of the image f are computed as follows (Thévenaz et al., 2000): TABLE 3.”
• A fast diffeomorphic image registration algorithm (2007, methodology): “It would be possible to use a higher-degree interpolation (see e.g. Thévenaz et al., 2000), but linear interpolation was used for speed.”
• REVERBERATION MAPPING RESULTS FOR FIVE SEYFERT 1 GALAXIES (2012, result): “This routine implements the publicly available Bspline interpolation code of Thévenaz et al. (2000) and produced better results with our images.”
• Deep Convolutional Neural Network for Inverse Problems in Imaging (2016, background): “Over the past decades, iterative reconstruction methods have become the dominant approach to solving inverse problems in imaging including denoising [1]–[4], deconvolution [5], [6], and interpolation [7], [8]. Thanks to robust regularizers such as total variation [1], [2], [5] and sparsity [9], prac”