Medical image registration: a review

By Francisco P. M. Oliveira, João Manuel Ribeiro da Silva Tavares. 2014

796 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Thomas Martin Deserno
2. Dorin Comaniciu
3. Qingwu Hu
4. Daniel A. Beard
5. Mingyao Ai
6. Benjamin Berkels
7. Jiayuan Li
8. András P. Keszei

In Computer Vision and Pattern Recognition

1. Marc Niethammer
2. Roland Kwitt
3. Xiao Yang
4. Shun Miao
5. Rui Liao
6. Daniel Gerlich
7. Sasa Grbic
8. Luc Van Gool

Citations in the corpus

• Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “Image registration techniques find optimal image transformations to correct for such artifacts (The´venaz et al., 1998; Oliveira and Tavares, 2012).”
• Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach (2017, background): “Due to the importance of image registration, a large number of different approaches have been proposed [1, 37, 38, 39].”
• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “A large number of software tools have been developed for MRI-based registration (Oliveira and Tavares, 2014).”
• Big Data Analytics in Healthcare (2015, background): “Registration/mapping Aligning consecutive slices/frames from one scan or corresponding images from different modalities [67, 68].”
• RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, methodology): “However, even if the matching is successful, the NCMs are also small, i.e., 24, 24, and 23.”
• An Artificial Agent for Robust Image Registration (2016, methodology): “The images are brought into the same coordinate system via various transformation models, e.g. rigid, affine, parametric splines, and dense motion fields (Oliveira and Tavares 2014).”
• Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “In recent years, the use of intensity-based registration methods has been growing compared to the feature-based methods [16], and this trend can be seen in the methods implemented in the tools reviewed in this paper.”