Multimodality image registration using an extensible information metric and high dimensional histogramming

Authors:
Jie Zhang;Anand Rangarajan
Affiliations:
Dept. of Computer & Information Science & Engineering, University of Florida, Gainesville, FL;Dept. of Computer & Information Science & Engineering, University of Florida, Gainesville, FL
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

Citing 7
Cited 3

Elements of information theory

Elements of information theory
Selectivity Estimation Without the Attribute Value Independence Assumption

VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
3D Multi-Modality Medical Image Registration Using Feature Space Clustering

CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Multi-variate Mutual Information for Registration

MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Alignment by maximization of mutual information

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Incorporating Connected Region Labelling into Automatic Image Registration Using Mutual Information

MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Improved Fast Gauss Transform and Efficient Kernel Density Estimation

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2

Comparing pairwise and simultaneous joint registrations of decorrelating interval exams using entropic graphs

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Image Comparison by Compound Disjoint Information with Applications to Perceptual Visual Quality Assessment, Image Registration and Tracking

International Journal of Computer Vision
Information-theoretic multi-modal image registration based on the improved fast Gauss transform: application to brain images

MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

We extend an information metric from intermodality (2-image) registration to multimodality (multiple-image) registration so that we can simultaneously register multiple images of different modalities. And we also provide the normalized version of the extensible in- formation metric, which has better performance in high noise situations. Compared to mutual information which can even become negative in the multiple image case, our metric can be easily and naturally extended to multiple images. After using a new technique to effciently compute high dimensional histograms, the extensible information metric can be effciently computed even for multiple images. To showcase the new measure, we compare the results of direct multimodality registration using high-dimensional histogramming with repeated intermodality registration. We find that registering 3 images simultaneously with the new metric is more accurate than pair-wise registration on 2D images obtained from synthetic magnetic resonance (MR) proton density (PD), MR T2 and MR T1 3D volumes from Brain Web. We perform the unbiased registration of 5 multimodality images of anatomy, CT, MR PD, T1 and T2 from Visible Human Male Data with the normalized metric and high-dimensional histogramming. Our results demonstrate the effcacy of the metrics and high-dimensional histogramming in affine, multimodality image registration.