Intensity-Based Image Registration by Minimizing Residual Complexity

By Andriy Myronenko, Xubo B. Song. IEEE Transactions on Medical Imaging, 2010

341 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a8abf9f4becde6bdc10c9a02a183e6cf591a1062

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. Nikos Paragios
2. Aristeidis Sotiras
3. Christos Davatzikos
4. Mark Jenkinson
5. João Manuel Ribeiro da Silva Tavares
6. Francisco P. M. Oliveira
7. M. Brady
8. Mattias Paul Heinrich

Citations in the corpus

• Deformable Medical Image Registration: A Survey (2013, background): “Myronenko and Song proposed to use residual complexity (RC) to account for complex spatially-varying intensity distortions [327].”
• MRF-Based Deformable Registration and Ventilation Estimation of Lung CT (2013, background): “The coding complexity of the residual image is minimized in [37] to estimate a”
• Medical image registration: a review (2014, background): “Recently, to overcome these requirements, a new similarity measure, called the residual complexity, was proposed in Myronenko and Song (2010).”