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Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks

Authors
 Byunghwan Jeon  ;  Yeonggul Jang  ;  Hackjoon Shim  ;  Hyuk-Jae Chang 
Citation
 Pattern Recognition, Vol.96 : e106958, 2019 
Journal Title
 Pattern Recognition 
ISSN
 0031-3203 
Issue Date
2019
Keywords
Computed tomography angiography ; Bayesian ; Localization ; Coronary artery ; Multiple target ; Curve analysis ; Curvature and torsion
Abstract
We propose a robust method for the identification of coronary arteries in computed tomography angiography (CTA) images. Utilizing geometric relations among the target and reference objects, which are assumed to follow a Gaussian distribution, an anatomic and geometric model is designed by Bayesian inference, which provides robust geometric priors for the target object localization. As a prerequisite process for the identification of coronary arteries, partially broken coronary artery segments found in CTA images are grouped and reconnected by geometric analysis of higher order curves connecting the broken segments. The geometric properties such as curvature and torsion represent naturalness and consistency between the vessel segments. As a problem to identify coronary arteries from CTA images, we demonstrate the robustness and accuracy of the proposed method in comparison with existing methods including commercial workstations on a variety of CTA cases.
Full Text
https://www.sciencedirect.com/science/article/pii/S0031320319302559
DOI
10.1016/j.patcog.2019.07.003
Appears in Collections:
5. Research Institutes (연구소) > Yonsei Cardiovascular Research Institute (심혈관연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Shim, Hack Joon(심학준)
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Jeon, Byung Hwan(전병환)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/174775
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