Medical image registration using mutual information

By Frederik Maes, Dirk Vandermeulen, Paul Suetens. Proceedings of the IEEE, 2003

497 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e881bb9696179e4044c73938579186ac2847327c

How researchers see this paper

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

In Aerospace Engineering

1. Xiaojie Guo
2. Jiayi Ma
3. Xingyu Jiang
4. Guobao Xiao
5. Linfeng Tang
6. Zhenfeng Shao
7. A. Gholipour
8. Habib Zaidi

In Biomedical Engineering

1. Linfeng Tang
2. Jiayi Ma
3. Jun Huang
4. Yong Ma
5. Kaundinya S. Gopinath
6. A. Gholipour
7. Habib Zaidi
8. João Manuel Ribeiro da Silva Tavares

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Linfeng Tang
3. Yong Ma
4. Jun Huang
5. Xiaojie Guo
6. Zhenfeng Shao
7. Yuxin Deng
8. Guobao Xiao

Citations in the corpus

• Universal Estimation of Information Measures for Analog Sources (2009, background): “Image registration based on mutual information has been investigated for medical imaging in [38, 160, 164, 241, 248] with focus on different aspects of the registration process.”
• A review of multimodal image matching: Methods and applications (2021, background): “[347] recognized that the MI measure gave rise to revolutionary breakthroughs in the MMIR task.”
• PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques (2010, methodology): “Mutual information This is a measure of uncertainty (entropy), which is routinely used with registration algorithms [196]; however, the metric is applicable to segmentation analysis as well [197].”
• SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness (2022, background): “, normalized correlation coefficient (NCC) [59] and mutual information (MI) [60].”
• Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain (2013, background): “For example, X-ray and computed tomography (CT) can provide dense structures like bones and implants with less distortion , but it cannot detect physiological changes [1].”
• Brain Functional Localization: A Survey of Image Registration Techniques (2007, background): “Recent survey papers on image registration [17], [18] have presented extensive reviews on mutual-information-based medical image registration.”
• Medical image registration: a review (2014, methodology): “, 2004), and a study on medical image registration based on mutual information is presented in (Maes et al., 2003).”