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Article

A Novel Method for Lung Image Processing Using Complex Networks

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Department of Computer and Information Technology, Automation and Computers Faculty, “Politehnica” University of Timișoara, Vasile Pârvan Blvd. No. 2, 300223 Timișoara, Romania
2
Pulmonology Department, ‘Victor Babes’ University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timișoara, Romania
3
Center for Research and Innovation in Precision Medicine of Respiratory Diseases (CRIPMRD), ‘Victor Babes’, University of Medicine and Pharmacy, 300041 Timișoara, Romania
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Department of Radiology and Medical Imaging, ‘Victor Babes’ University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timișoara, Romania
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Research Center for Pharmaco-Toxicological Evaluations, ‘Victor Babes’ University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timișoara, Romania
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Emilio Quaia
Tomography 2022, 8(4), 1928-1946; https://doi.org/10.3390/tomography8040162
Received: 14 June 2022 / Revised: 20 July 2022 / Accepted: 22 July 2022 / Published: 27 July 2022
(This article belongs to the Section Artificial Intelligence in Medical Imaging)
The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in three dimensions: emphysema, ground glass opacity, and consolidation. This method was evaluated on a 60-patient lot and the results showed a clear, quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively. View Full-Text
Keywords: diffuse interstitial lung disease; complex networks; model; HRCT diffuse interstitial lung disease; complex networks; model; HRCT
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MDPI and ACS Style

Broască, L.; Trușculescu, A.A.; Ancușa, V.M.; Ciocârlie, H.; Oancea, C.-I.; Stoicescu, E.-R.; Manolescu, D.L. A Novel Method for Lung Image Processing Using Complex Networks. Tomography 2022, 8, 1928-1946. https://doi.org/10.3390/tomography8040162

AMA Style

Broască L, Trușculescu AA, Ancușa VM, Ciocârlie H, Oancea C-I, Stoicescu E-R, Manolescu DL. A Novel Method for Lung Image Processing Using Complex Networks. Tomography. 2022; 8(4):1928-1946. https://doi.org/10.3390/tomography8040162

Chicago/Turabian Style

Broască, Laura, Ana Adriana Trușculescu, Versavia Maria Ancușa, Horia Ciocârlie, Cristian-Iulian Oancea, Emil-Robert Stoicescu, and Diana Luminița Manolescu. 2022. "A Novel Method for Lung Image Processing Using Complex Networks" Tomography 8, no. 4: 1928-1946. https://doi.org/10.3390/tomography8040162

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