International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 488 - 495

Supervised Filter Learning for Coronary Artery Vesselness Enhancement Diffusion in Coronary CT Angiography Images

Authors
Hengfei Cui1, 2, *
1National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
2Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
Corresponding Author
Hengfei Cui
Received 16 January 2020, Accepted 20 April 2020, Available Online 6 May 2020.
DOI
10.2991/ijcis.d.200422.001How to use a DOI?
Keywords
Computed tomography angiography; Coronary artery filtering; Vesselness enhancement; Machine learning; Vessel segmentation
Abstract

In medical imaging, vesselness diffusion is usually performed to enhance the vessel structures of interest and reduce background noises, before vessel segmentation and analysis. Numerous learning-based techniques have recently become very popular for coronary artery filtering due to their impressive results. In this work, a supervised machine learning method for coronary artery vesselness diffusion with high accuracy and minimal user interaction is designed. The fully discriminative filter learning method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Experimental results demonstrate that this scheme achieves good isotropic filtering performances on both synthetic and real patient Coronary Computed Tomography Angiography (CCTA) datasets. Furthermore, region growing-based segmentation approach is performed over filtered images obtained by using different schemes. The proposed diffusion scheme is able to achieve higher average performance measures (87.8% ± 1.5% for Dice, 86.5% ± 1.3% for Precision and 88.5% ± 2.6% for Sensitivity). In conclusion, the developed diffusion method is capable of filtering coronary artery structures and suppressing nonvessel tissues, and can be further used in clinical practice as a real-time CCTA images preprocessing tool.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
488 - 495
Publication Date
2020/05/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200422.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Hengfei Cui
PY  - 2020
DA  - 2020/05/06
TI  - Supervised Filter Learning for Coronary Artery Vesselness Enhancement Diffusion in Coronary CT Angiography Images
JO  - International Journal of Computational Intelligence Systems
SP  - 488
EP  - 495
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200422.001
DO  - 10.2991/ijcis.d.200422.001
ID  - Cui2020
ER  -