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


PhD Thesis Defence


"Robust Multimodal Medical Image Registration and Statistical
Cerebrovascular Segmentation"

By

Miss Rui Gan


Abstract

In this thesis, we focus on two important medical image processing and
analysis problems: multimodal image registration and cerebrovascular
segmentation.

First, for the purpose of improving the robustness of image registration,
we propose two novel registration objective functions and a new technique
for better optimization performance. The first novel objective function is
a combination of the multi-dimensional mutual information and an angle
measure on the maximum distance-gradient (MDG) vector field. The MDG
vector field is a new spatial feature field designed for registration
tasks. It encodes both local edge information and globally defined spatial
information related to the intensity difference, the distance, and the
direction of a voxel to a certain object boundary point. This objective
function then integrates both the magnitude and the orientation
information of the MDG feature into the image registration process. The
other novel objective function can be obtained if the statistical joint
intensity mappings estimated from the pre-aligned training image pairs are
available. This a priori knowledge can then be used as reference to guide
the multi-resolution based registration process, by minimizing a new
Kullback-Leibler distance (KLD) based similarity measure between the
observed joint intensity distribution and the reference joint intensity
distribution. To achieve better optimization, which evidently affects the
robustness of a registration process, we propose a general technique based
on random transformation disturbance to assist optimization procedures to
avoid local optima, and hence to achieve robust optimization results. The
technique is based on different properties of the local neighborhood of a
global optimum and those of local optima.

Second, this thesis presents a statistical segmentation technique for the
extraction of vasculatures in three-dimensional rotational angiography
(3D-RA). This method uses maximum intensity projections (MIP) to improve
the accuracy and robustness of threshold estimation using the expectation
maximization (EM) algorithm. It is fully automatic and computationally
efficient.

Finally, encouraging experimental results of the proposed approaches on
both synthetic and clinical medical images are presented in the thesis.


Date:			Tuesday, 22 August 2006

Time:			2:00p.m.-4:00p.m.

Venue:			Room 5501
			Lifts 25-26

Chairman:		Prof. Xiao Ping Wang (MATH)

Committee Members:	Prof. Albert Chung (Supervisor)
			Prof. Huamin Qu
			Prof. Chi Keung Tang
			Prof. Pengcheng Shi (ECE)
			Prof. Pheng Ann Heng (Comp. Sci. & Engg., CUHK)


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