Learning-Based Dissimilarity Metric For Rigid and Non-Rigid Medical Image Registration

PhD Thesis Proposal Defence


Title: "Learning-Based Dissimilarity Metric For Rigid and Non-Rigid 
Medical Image Registration"

by

Mr. Wai King SO


Abstract:

Image registration is widely used in different areas. It plays an 
important role in medical image analysis, group analysis and statistical 
parametric mapping. For the medical image analysis, image registration is 
useful for diagnosis, treatment planning, treatment evaluation, and so on. 
All these applications are relied on a correct registration result to 
provide higher treatment quality, increase the precision of diagnosis, and 
reduce the workload of doctors. Thus, it is essential to improve the 
robustness and accuracy of image registration. According to the nature of 
the transformation, image registration can be categorized into two main 
classes: Rigid Registration and Non-rigid Registration. The objective of 
this proposal is to develop a novel learning-based dissimilarity metric 
for both rigid and non-rigid medical image registrations. This novel 
metric utilizes Bhattacharyya distances to measure the dissimilarity of 
the testing image pairs by incorporating the expected intensity 
distributions (priori knowledge) which learned from the registered 
training image pairs. The proposed dissimilarity metric can be easily 
adopted to the existing framework of rigid image registration whereas it 
is not trivial to apply it into the existing framework of non-rigid image 
registration. Therefore, an approximation of the proposed dissimilarity 
metric is also derived in this proposal such that the proposed metric can 
be applied to the Markov Random Field (MRF) modeled non-rigid image 
registration approach. By the help of Bhattacharyya distances, the priori 
knowledge and the MRF modeled registration framework, we believe that our 
novel learning-based dissimilarity metric can achieve higher robustness 
and accuracy, as compared with state-of-the-art approaches, in both rigid 
and non-rigid image registrations.


Date:			Thursday, 15 September 2016

Time:                  	12:30pm - 2:30pm

Venue:                  Room 4475
                         (lifts 25/26)

Committee Members:	Prof. Albert Chung (Supervisor)
  			Prof. Chi-Keung Tang (Chairperson)
 			Prof. Chiew-Lan Tai
  			Prof. Dit-Yan Yeung


**** ALL are Welcome ****