1 research outputs found
EREL Selection using Morphological Relation
This work concentrates on Extremal Regions of Extremum Level (EREL)
selection. EREL is a recently proposed feature detector aiming at detecting
regions from a set of extremal regions. This is a branching problem derived
from segmentation of arterial wall boundaries from Intravascular Ultrasound
(IVUS) images. For each IVUS frame, a set of EREL regions is generated to
describe the luminal area of human coronary. Each EREL is then fitted by an
ellipse to represent the luminal border. The goal is to assign the most
appropriate EREL as the lumen. In this work, EREL selection carries out in two
rounds. In the first round, the pattern in a set of EREL regions is analyzed
and used to generate an approximate luminal region. Then, the two-dimensional
(2D) correlation coefficients are computed between this approximate region and
each EREL to keep the ones with tightest relevance. In the second round, a
compactness measure is calculated for each EREL and its fitted ellipse to
guarantee that the resulting EREL has not affected by the common artifacts such
as bifurcations, shadows, and side branches. We evaluated the selected ERELs in
terms of Hausdorff Distance (HD) and Jaccard Measure (JM) on the train and test
set of a publicly available dataset. The results show that our selection
strategy outperforms the current state-of-the-art.Comment: 6 pages, 8 figures, accepted to be published in International
Conference on SMART MULTIMEDIA, 2018. The final authenticated publication is
available online at https://doi.org