Adaptive Stochastic Gradient Descent Optimisation for Image Registration

We present a stochastic gradient descent optimisation method for image registration with adaptive step size prediction. The method is based on the theoretical work by Plakhov and Cruz (J. Math. Sci. 120(1):964–973, 2004 ). Our main methodological contribution is the derivation of an image-driven mec...

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Bibliographic Details
Published in:	International journal of computer vision Vol. 81; no. 3; pp. 227 - 239
Main Authors:	Klein, Stefan, Pluim, Josien P. W., Staring, Marius, Viergever, Max A.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.03.2009 Springer Springer Nature B.V
Subjects:	Applied sciences Artificial Intelligence Computer Imaging Computer Science Computer science; control theory; systems Exact sciences and technology Image Processing and Computer Vision Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Programming theory Registration Studies Theoretical computing Vision Image registration Parameter selection Stochastic gradient descent Adaptive step sizes Optimisation Human Head Adaptive algorithm Probabilistic approach Similarity Image processing Lung Model driven architecture Respiratory system Adaptive method Characteristic function Gradient descent Stochastic gradient descent, Adaptive step sizes, Parameter selection Descent method Metric Gradient method Prostate Step function
ISSN:	0920-5691, 1573-1405
Online Access:	Get full text
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