Background
Carotid atherosclerosis is one of the major causes of stroke. The determination of
the intima-media thickness, the identification of carotid atherosclerotic plaque,
and the classification of the different stenoses are considered as important parameters
for the assessment of atherosclerotic diseases. The aim of this work is to segment
the plaques and to allow a better comprehension of carotid atherosclerosis.
Methods
We considered 44 subjects, 22 with and 22 without the presence of plaques in the carotid
axis, and we applied the snake algorithm.
Results
The resulting interclass correlation coefficients (ICCs) were significant for all
3 parameters (mean echogenicity: ICC1 = .78 [95%CI: .55-0.90]; perimeter: ICC2 = .81 [95%CI: .61-0.92]; area: ICC3 = .89 [95%CI: .75-0.95]). The diagnostic accuracy was 82%, with an appropriate cutoff
value of 224.5, sensitivity of 79%, and specificity of 85%.
Conclusions
In this study, we developed an automatic method to identify the carotid plaque. Our
results showed that an automatic system of image segmentation could be used to identify,
characterize, and measure atherosclerotic carotid plaques.
Key Words
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Article info
Publication history
Published online: October 31, 2016
Accepted:
September 30,
2016
Received in revised form:
September 14,
2016
Received:
July 25,
2016
Identification
DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2016.09.045
Copyright
© 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.
