"A Stochastic Quasi-Newton Method for Non-rigid Image Registration"

Yuchuan Qiao, Z. Sun, Boudewijn P.F. Lelieveldt and Marius Staring


Image registration is often very slow because of the high dimensionality of the images and complexity of the algorithms. Adaptive stochastic gradient descent (ASGD) outperforms deterministic gradient descent and even quasi-Newton in terms of speed. This method, however, only exploits first-order information of the cost function. In this paper, we explore a stochastic quasi-Newton method (s-LBFGS) for non-rigid image registration. It uses the classical limited memory BFGS method in combination with noisy estimates of the gradient. Curvature information of the cost function is estimated once every L iterations and then used for the next L iterations in combination with a stochastic gradient. The method is validated on follow-up data of 3D chest CT scans (19 patients), using a B-spline transformation model and a mutual information metric. The experiments show that the proposed method is robust, efficient and fast. s-LBFGS obtains a similar accuracy as ASGD and deterministic LBFGS. Compared to ASGD the proposed method uses about 5 times fewer iterations to reach the same metric value, resulting in an overall reduction in run time of a factor of two. Compared to deterministic LBFGS, s-LBFGS is almost 500 times faster.



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Copyright © 2015 by the authors. Published version © 2015 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.


BibTeX entry

author = "{Yuchuan Qiao and Z. Sun and Boudewijn P.F. Lelieveldt and Marius Staring}",
title = "{A Stochastic Quasi-Newton Method for Non-rigid Image Registration}",
booktitle = "{Medical Image Computing and Computer-Assisted Intervention}",
editor = "{N. Navab and J. Hornegger and W.M. Wells and A.F. Frangi}",
address = "{Munchen,Germany}",
series = "{Lecture Notes in Computer Science}",
volume = "{9350}",
pages = "{297 - 304}",
month = "{September}",
year = "{2015}",

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