New Similarity Measures and Deformation Optimization Comparisons for Medical Image Registration

MPhil Thesis Defence


Title: "New Similarity Measures and Deformation Optimization Comparisons for Medical 
Image Registration"

By

Mr. Wai-King So


Abstract

Image registration is widely used in different areas, including medical image 
analysis and image processing. In this thesis, we introduce a new similarity 
function for image registration and compare two optimization methods of Markov 
Random Field (MRF) based non-rigid image registration. The novel similarity 
function is based on a priori knowledge of the joint intensity distribution of 
a pre-aligned image pair. We have evaluated the proposed similarity measures 
with 3600 randomized rigid registration experiments on CT-T1 brain image pairs 
from the Retrospective Image Registration Evaluation (RIRE) project. The 
results show that the proposed similarity measures give significant improvement 
on the registration accuracies and success rates as compared with the mutual 
information (MI) based method. We have also tested the similarity measures and 
their derivatives on seven multi-modal non-rigid image registration experiments 
under Free-Form Deformation (FFD) registration framework and compared the 
results obtained by using the MI based FFD and the conventional KLD based FFD. 
The experimental results demonstrate that our method gives remarkable 
improvement on the registration accuracy. In addition, we have compared two 
optimizations, graph cut and linear programming, of MRF based non-rigid 
registration. The experimental results show that graph cut is slower than 
linear programming but it can provide higher accuracy and use less memory.


Date:			Tuesday, 18 August 2009

Time:			4:00pm-6:00pm

Venue:			Room 3315
 			Lifts 17-18

Committee Members:	Dr. Albert Chung (Supervisor)
 			Dr. Huamin Qu (Chairperson)
 			Prof. Dit-Yan Yeung


**** ALL are Welcome ****