A Unifying MAP-MRF Framework for Deriving New Point Similarity Measures for Intensity-based 2D-3D Registration

Authors:
Guoyan Zheng;Xuan Zhang
Affiliations:
University of Bern, Stauffacherstrasse 78, CH-3014 Bern, Switzerland;University of Bern, Stauffacherstrasse 78, CH-3014 Bern, Switzerland
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

Citing 0
Cited 2

Automatic Robust Medical Image Registration Using a New Democratic Vector Optimization Approach with Multiple Measures

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Intensity based image registration by minimizing exponential function weighted residual complexity

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D-3D registration of X-ray fluoroscopy to CT images. This paper presents a unifying MAP-MFR framework for rationally deriving point similarity measures based on Bayes theorem. Three new similarity measures derived from this framework are presented and evaluated using a phantom and a human cadaveric specimen. Their behaviors are compared to other well-known similarity measures and the comparison results are reported. Combining any one of the new similarity measures with a previously introduced spline-based multiresolution 2D-3D registration scheme, we develop a fast and accurate registration algorithm. We report their capture ranges, converging speeds, and registration accuracies.