Amr R. Abdel-Dayem and Mahmoud R. El-Sakka, "Segmentation of
Carotid Artery Ultrasound Images Using Graph Cuts",
International Journal for Computational Vision and
Biomechanics, Vol. 3, No. 1, pp. 61-71, 2010.
This paper proposes a scheme for segmenting carotid artery
ultrasound images using graph cuts segmentation approach.
Region homogeneity constraints, edge information and domain
specific information are incorporated during the segmentation
process. A graph with two terminals (source and sink) is
formed by considering every pixel as a graph node. Each pair
of neighbouring nodes is connected by a weighted edge, where
the weight is set to a value proportional to the intensity of
the gradient along them. Moreover, each graph node is
connected to the source and the sink terminals with weights
that reflect the confidence that the corresponding pixel
belongs to the object and the background, respectively. The
segmentation problem is solved by finding the minimum cut
through the constructed graph. Experiments using a dataset
comprised of 40 B-mode carotid artery ultrasound images
demonstrates good segmentation results with (on average)
0.677 overlap with the gold standard images, 0.690 precision,
and 0.983 sensitivity.