Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A patient-specific coronary density estimate
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Automated carotid artery distensibility measurements from CTA using nonrigid registration
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Early diagnosis of dementia based on intersubject whole-brain dissimilarities
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Automated registration of whole-body follow-up MicroCT data of mice
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Preconditioned stochastic gradient descent optimisation for monomodal image registration
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Hierarchical vs. simultaneous multiresolution strategies for nonrigid image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Computer Methods and Programs in Biomedicine
Computer Vision and Image Understanding
Computers and Electrical Engineering
Hi-index | 0.00 |
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 mechanism to select proper values for the most important free parameters of the method. The selection mechanism employs general characteristics of the cost functions that commonly occur in intensity-based image registration. Also, the theoretical convergence conditions of the optimisation method are taken into account. The proposed adaptive stochastic gradient descent (ASGD) method is compared to a standard, non-adaptive Robbins-Monro (RM) algorithm. Both ASGD and RM employ a stochastic subsampling technique to accelerate the optimisation process. Registration experiments were performed on 3D CT and MR data of the head, lungs, and prostate, using various similarity measures and transformation models. The results indicate that ASGD is robust to these variations in the registration framework and is less sensitive to the settings of the user-defined parameters than RM. The main disadvantage of RM is the need for a predetermined step size function. The ASGD method provides a solution for that issue.