Multi-modal image registration using dirichlet-encoded prior information

Authors:
Lilla Zöllei;William Wells
Affiliations:
MIT, CSAIL, Cambridge, MA;MIT, CSAIL, Cambridge, MA
Venue:
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Year:
2006

Citing 5
Cited 2

Multi-modal Volume Registration Using Joint Intensity Distributions

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-modal Image Registration by Minimising Kullback-Leibler Distance

MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
A unified information theoretic framework for pair- and group-wise registration of medical images

A unified information theoretic framework for pair- and group-wise registration of medical images
Maximum a posteriori local histogram estimation for image registration

MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Learning based non-rigid multi-modal image registration using Kullback-Leibler divergence

MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II

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
Registration of cardiac SPECT/CT data through weighted intensity co-occurrence priors

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new objective function for the registration of multi-modal medical images. Our novel similarity metric incorporates both knowledge about the current observations and information gained from previous registration results and combines the relative influence of these two types of information in a principled way. We show that in the absence of prior information, the method reduces approximately to the popular entropy minimization approach of registration and we provide a theoretical comparison of incorporating prior information in our and other currently existing methods. We also demonstrate experimental results on real images.