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

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Learning-Based Dissimilarity Measure 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 thesis is to develop a novel learning-based dissimilarity measure for 
both rigid and non-rigid medical image registrations. This novel measure 
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 measure 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 measure is also derived in 
this thesis such that the proposed measure 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 measure can achieve higher robustness and accuracy, as 
compared with state-of-the-art approaches, in both rigid and non-rigid 
image registrations.


Date:			Wednesday, 18 January 2017

Time:			2:30pm - 4:30pm

Venue:			Room 3494
 			Lifts 25/26

Chairman:		Prof. Melody Chao (MGMT)

Committee Members:	Prof. Albert Chung (Supervisor)
 			Prof. Pedro Sander
 			Prof. Chiew-Lan Tai
 			Prof. Shing-Yu Leung (MATH)
 			Prof. Pheng-Ann Heng (Comp. Sci. & Engg., CUHK)


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