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"Preconditioned Stochastic Gradient Descent Optimisation for Monomodal Image Registration"
Stefan Klein, Marius Staring, Patrik Andersson and Josien P.W. Pluim
Abstract
We present a stochastic optimisation method for intensity-based monomodal image registration. The method is based on a Robbins-Monro stochastic gradient descent method with adaptive step size estimation, and adds a preconditioning matrix. The derivation of the preconditioner is based on the observation that, after registration, the deformed moving image should approximately equal the fixed image. This prior knowledge allows us to approximate the Hessian at the minimum of the registration cost function, without knowing the coordinate transformation that corresponds to this minimum. The method is validated on 3D fMRI time-series and 3D CT chest follow-up scans. The experimental results show that the preconditioning strategy improves the rate of convergence.
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Copyright © 2011 by the authors.
Published version © 2011 by Springer Lecture Notes in Computer Science.
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @inproceedings{Klein:2011, |
| author |
= {Klein, Stefan and Staring, Marius and Andersson, Patrik and Pluim, Josien P.W.}, |
| title |
= {Preconditioned Stochastic Gradient Descent Optimisation for Monomodal Image Registration}, |
| booktitle |
= {Medical Image Computing and Computer-Assisted Intervention}, |
| editor |
= {Fichtinger, G. and Martel, A. and Peters, T.}, |
| address |
= {Toronto, Canada}, |
| series |
= {Lecture Notes in Computer Science}, |
| volume |
= {6892}, |
| pages |
= {549 - 556}, |
| month |
= {September}, |
| year |
= {2011}, |
| } |
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