Vascular segmentation in magnetic resonance angiography

PhD Thesis Proposal Defence


Title: "Vascular segmentation in magnetic resonance angiography"

by

Mr. Wai-Kong Law


Abstract:

Clinical assessment of vasculatures is essential for the detection and
treatment of vascular diseases which can be potentially fatal. To
facilitate clinical assessment of blood vessels, there is a growing need
of developing automated vessel segmentation schemes based on magnetic
resonance angiographic (MRA) images. A vast number of approaches have been
proposed in the past decade for the segmentation of vascular structures in
MRA images. These approaches were devised according to different
assumptions on the shape of blood vessels and different underlying prior
knowledge about the desired imaging modalities. The development of these
approaches aims at delivering more accurate and robust segmentation
results. Nonetheless, these approaches face different technical challenges
that prohibit them from being widely employed in the clinical environment.
The challenges include significant contrast variation of vessel boundaries
in MRA images, the excessive computation time required by some algorithms
and the complicated geometry of vascular structures. These challenges
motivate us to propose three novel edge detection and vascular
segmentation methods.

In the first proposed method, vessel segmentation is performed grounded on
the edge detection responses given by the weighted local variance-based
edge detector. This detector is robust against large intensity contrast
changes and capable of returning accurate detection responses on low
contrast edges. Our second method is an efficient implementation of a well
founded vessel detection approach. The proposed efficient implementation
is a thousand times faster than the conventional implementation without
segmentation performance deterioration. The third method is a curvilinear
structure descriptor which is robust against the disturbance induced by
closely located objects. Preliminary experimental results show that the
proposed methods are very suitable for vascular segmentation in MRA
images.


Date:  			Tuesday, 18 May 2010

Time:           	2:00pm - 4:00pm

Venue:          	Room 3494
 			lifts 25/26

Committee Members:      Dr. Albert Chung (Supervisor)
 			Dr. Chiew-Lan Tai (Chairperson)
 			Dr. Huamin Qu
 			Dr. Pedro Sander


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