Sherif Moursi and Mahmoud R. El-Sakka, "Semi-Automatic
Snake-Based Segmentation of Carotid Artery Ultrasound Images",
Communications of the ACS Magazine, Vol. 2, No. 2 (32 pages),
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 techniques (also known as
snakes or deformable model) are characterized by their
robustness to both image noise and boundary gaps. Hence, they
are suitable to be used to segment noisy poor quality
ultrasound images. One of the major issues of active
contouring methods is their 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 paper presents an efficient algorithm for extracting
carotid artery lumens in ultrasound images. It starts by
utilizing a rule-based scheme to generate an initial contour
for the lumen. This contour is refined using a snake scheme,
after carefully adjusting its energies. Our algorithm reduces
the user interaction, as the user is only required to place a
seed point inside the region of interest. It is worth
mentioning that our proposed initial contour generation scheme
can be easily integrated as an independent module with any
active contouring algorithm.
Sensitivity, precision rate, and overlap ratio are utilized to
assess the performance of the proposed scheme. The results
show that the extracted initial and final contours have a good
overlap with contours that are manually segmented by an