Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Guerrero:2012:10.1007/978-3-642-35428-1_27,
author = {Guerrero, R and Donoghue, CR and Pizarro, L and Rueckert, D},
doi = {10.1007/978-3-642-35428-1_27},
pages = {218--225},
title = {Learning correspondences in knee MR images from the osteoarthritis initiative},
url = {http://dx.doi.org/10.1007/978-3-642-35428-1_27},
year = {2012}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration of images of the knee is challenging to achieve using intensity based registration algorithms. Problems arise due to large anatomical inter-subject differences which causes registrations to fail to converge to an accurate solution. In this work we propose learning correspondences in pairs of images to match self-similarity features, that describe images in terms of their local structure rather than their intensity. We use RANSAC as a robust model estimator. We show a substantial improvement in terms of mean error and standard deviation of 2.13mm and 2.47mm over intensity based registration methods, when comparing landmark alignment error. © 2012 Springer-Verlag.
AU - Guerrero,R
AU - Donoghue,CR
AU - Pizarro,L
AU - Rueckert,D
DO - 10.1007/978-3-642-35428-1_27
EP - 225
PY - 2012///
SN - 0302-9743
SP - 218
TI - Learning correspondences in knee MR images from the osteoarthritis initiative
UR - http://dx.doi.org/10.1007/978-3-642-35428-1_27
ER -