  Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that
facilitates observing and appraising the vessel wall structure of the human
coronary arteries. Segmentation of arterial wall boundaries from the IVUS
images is not only crucial for quantitative analysis of the vessel walls and
plaque characteristics, but is also necessary for generating 3D reconstructed
models of the artery. The aim of this study is twofold. Firstly, we investigate
the feasibility of using a recently proposed region detector, namely Extremal
Region of Extremum Level (EREL) to delineate the luminal and media-adventitia
borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region
selection strategy to label two ERELs as lumen and media based on the stability
of their textural information. We extensively evaluated our selection strategy
on the test set of a standard publicly available dataset containing 326 IVUS
B-mode images. We showed that in the best case, the average Hausdorff Distances
(HD) between the extracted ERELs and the actual lumen and media were $0.22$ mm
and $0.45$ mm, respectively. The results of our experiments revealed that our
selection strategy was able to segment the lumen with $\le 0.3$ mm HD to the
gold standard even though the images contained major artifacts such as
bifurcations, shadows, and side branches. Moreover, when there was no artifact,
our proposed method was able to delineate media-adventitia boundaries with
$0.31$ mm HD to the gold standard. Furthermore, our proposed segmentation
method runs in time that is linear in the number of pixels in each frame. Based
on the results of this work, by using a 20 MHz IVUS probe with controlled
pullback, not only can we now analyze the internal structure of human arteries
more accurately, but also segment each frame during the pullback procedure
because of the low run time of our proposed segmentation method.
