Sherif Moursi, December 2007, "Semi-Automatic Snake Based
Segmentation of Carotid Artery Ultrasound Images", Computer
Science Department, University of Western Ontario, Canada.
M.Sc. Thesis Abstract
Carotid ultrasound imaging is one of the clinical diagnostic
procedures that can be employed to detect plaque buildup at
the carotid artery walls. It is an inexpensive and
non-invasive procedure that has no known side effects. Yet,
the acquired ultrasound images have poor quality and contain
a lot of noise. Active contouring segmentation technique
(also known as snakes or deformable model) is characterized
by its robustness to both image noise and boundary gaps.
Hence it is suitable to be used as a segmentation technique
in noisy poor quality ultrasound images.
One of the major issues of active contouring method is its
sensitivity to the initial contour that is provided by the
user. Unless it is drawn close enough to the actual contour,
it may lead to unsatisfactory results. Thus, most active
contour algorithms require considerable user interaction to
provide a good initial contour. This thesis presents a fast
and efficient algorithm for extracting carotid artery lumens
in ultrasound images. Our algorithm reduces the user
interaction as the user is only required to place a seed
point inside the region of interest. It utilizes our proposed
rule-based initial contour, with carefully adjusting snake
energies to refine this initial contour.
Sensitivity, precision rate, and overlap ratio have been used
to measure the accuracy of the extracted contours. The results
show that the extracted initial and final contours have a
good overlap with contours that are manually segmented by an
experienced clinician. Furthermore, our proposed initial
contour generation scheme can be used as an independent
module with any active contouring algorithm to produce