Preprint
Article
A Novel Method for Lung Image Processing Using Complex Networks
This version is not peer-reviewed.
Submitted:
08 July 2022
Posted:
11 July 2022
You are already at the latest version
A peer-reviewed article of this preprint also exists.
Abstract
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 3 dimensions: emphysema, ground glass opacity and consolidation. This method was evaluated on a 60 patient lot and the results show 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.
Keywords:
diffuse interstitial lung disease
; complex networks
; model
; HRCT
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Downloads
187
Views
134
Comments
0
Subscription
Notify me about updates to this article or when a peer-reviewed version is published.
MDPI Initiatives
Important Links
© 2025 MDPI (Basel, Switzerland) unless otherwise stated