At the core of most image registration problems is determining a spatial transformation that relates the physical coordinates of two or more images. Registration methods have become ubiquitous in many quantitative imaging applications. They represent an essential step for many biomedical and bioengineering applications…
Contents
1 Introduction and motivation
1.1 Problem statement
1.2 Summary of Dissertation
2 Background
2.1 Feature-based methods
2.2 Intensity-based methods
2.2.1 Problem statement
2.2.2 Spatial transformations
2.2.3 Image dissimilarity measures
2.2.4 Image interpolation and approximation
2.2.5 Numerical optimization strategies
3 Overview of Contributions
3.1 Image formation model
3.2 Measuring image similarity
3.3 Registration of diffusion weighted MRI’s
3.4 Motion correction in optical mapping
3.5 Post-registration noise variance estimates
4 Measuring image similarity for image registration
4.1 Introduction
4.2 Theory
4.2.1 Covariance properties of interpolated signals
4.2.2 Optimization of L2-based similarity measures
4.2.3 Optimization of correlation-based similarity measures
4.2.4 Optimization of mutual information
4.3 Methods
4.4 Results
4.5 Discussion
4.6 Summary and Conclusions
4.7 Appendix A
4.8 Appendix B
4.9 Appendix C
4.9.1 Joint entropy between two images
4.9.2 Do oscillations in H(Sc) and −H(Sc, T) cancel out?
4.9.3 Mixture model for MRI
5 Comprehensive Approach for Correction of Motion and Distor-tion in Diffusion Weighted MRI
5.1 Introduction
5.2 Materials and Methods
5.2.1 Pulse sequence and MRI parameters
5.2.2 Formulation of the spatial transformation model
5.2.3 Effects of eddy currents
5.2.4 Cost function…
Source: University of Maryland
projects, dissertation, thesis, project report