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An Optimized Technique of Tree Generation for Artery/Vein Separation in Non-Contrast CT Imaging

by Arya Varghese, J. Ramya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 18
Year of Publication: 2013
Authors: Arya Varghese, J. Ramya
10.5120/12640-8989

Arya Varghese, J. Ramya . An Optimized Technique of Tree Generation for Artery/Vein Separation in Non-Contrast CT Imaging. International Journal of Computer Applications. 72, 18 ( June 2013), 5-10. DOI=10.5120/12640-8989

@article{ 10.5120/12640-8989,
author = { Arya Varghese, J. Ramya },
title = { An Optimized Technique of Tree Generation for Artery/Vein Separation in Non-Contrast CT Imaging },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 18 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number18/12640-8989/ },
doi = { 10.5120/12640-8989 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:14.380095+05:30
%A Arya Varghese
%A J. Ramya
%T An Optimized Technique of Tree Generation for Artery/Vein Separation in Non-Contrast CT Imaging
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 18
%P 5-10
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Arterial and venous trees separation in non-contrast pulmonary CT imaging facilitates extraction of quantitative measures at different tree levels. Reconstruction and separation of arterial and venous vascular trees is an essential step for diagnostic application systems used for maladies such as pulmonary embolism and coronary artery disease[1]. Although, higher tree generations of vasculature, arteries and veins are indistinguishable by their intensity values, automatic artery-vein (AV) reconstruction and separation still remains a challenging problem in computational imaging and geometry processing due to patient specific structural abnormalities of vascular trees. This paper presents a novel technique of multi-scale fuzzy enhanced topo-morphologic opening algorithm (MSFTMO) to separate artery and vein from non-contrast CT images, which can be used for diagnosing arteriosclerosis as well. The algorithm combines fuzzy distance transform, a morphologic feature, with a topologic connectivity and a new morphological reconstruction step to iteratively open multi-scale fusions starting at large scales, progressing towards smaller scales[1][2]. The algorithm is applied on fuzzy segmentation results via a small amount of intuitive interactions using an efficient graphical user interface.

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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy segmentation Fuzzy connectivity Local Update and user interface multi-scale fuzzy enhanced topo-morphologic opening algorithm arteriosclerosis

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