Likelihood maximization approach to image registration

Authors:
Yang-Ming Zhu;S. M. Cochoff
Affiliations:
Nucl. Medicine Div., Philips Med. Syst., Cleveland, OH, USA;-
Venue:
IEEE Transactions on Image Processing
Year:
2002

Citing 0
Cited 13

Mutual information-based registration of temporal and stereo retinal images using constrained optimization

Computer Methods and Programs in Biomedicine
Multimodality image registration by maximization of quantitative-qualitative measure of mutual information

Pattern Recognition
Robust feature-based registration using a Gaussian-weighted distance map and brain feature points for brain PET/CT images

Computers in Biology and Medicine
Multimodality Image Registration Using Spatial Procrustes Analysis and Modified Conditional Entropy

Journal of Signal Processing Systems
Robust and fast shell registration in PET and MR/CT brain images

Computers in Biology and Medicine
Image registration using geometric deformable model and penalized maximum likelihood

CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
A marginalized MAP approach and EM optimization for pair-wise registration

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Multi-dimensional mutual information based robust image registration using maximum distance-gradient-magnitude

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Multi-modal image registration using the generalized survival exponential entropy

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Multi-modality image registration using gradient vector flow intensity

Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Distance-Intensity for image registration

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Multi-modal image registration by quantitative-qualitative measure of mutual information (Q-MI)

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Volume sweeping and bodyline matching for automated prealignment in volumetric medical image registration

Computers in Biology and Medicine

Quantified Score

Hi-index	0.01

Visualization

Abstract

A likelihood maximization approach to image registration is developed in this paper. It is assumed that the voxel values in two images in registration are probabilistically related. The principle of maximum likelihood is then exploited to find the optimal registration: the likelihood that given image f, one has image g and given image g, one has image f is optimized with respect to registration parameters. All voxel pairs in the overlapping volume or a portion of it can be used to compute the likelihood. A knowledge-based method and a self-consistent technique are proposed to obtain the probability relation. In the knowledge-based method, prior knowledge of the distribution of voxel pairs in two registered images is assumed, while such knowledge is not required in the self-consistent method. The accuracy and robustness of the likelihood maximization approach is validated by single modality registration of single photon emission computed tomographic (SPECT) images and magnetic resonance (MR) images and by multimodality registration (MR/SPECT). The results demonstrate that the performance of the likelihood maximization approach is comparable to that of the mutual information maximization technique. Finally the relationship between the likelihood approach and the entropy, conditional entropy, and mutual information approaches is discussed.