Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography.
computed tomography
imaging conditions
lung lesions
morphology
quantitative imaging
Journal
Journal of medical imaging (Bellingham, Wash.)
ISSN: 2329-4302
Titre abrégé: J Med Imaging (Bellingham)
Pays: United States
ID NLM: 101643461
Informations de publication
Date de publication:
Jan 2019
Jan 2019
Historique:
received:
14
08
2018
accepted:
01
03
2019
entrez:
5
4
2019
pubmed:
5
4
2019
medline:
5
4
2019
Statut:
ppublish
Résumé
We propose to characterize the bias and variability of quantitative morphology features of lung lesions across a range of computed tomography (CT) imaging conditions. A total of 15 lung lesions were simulated (five in each of three spiculation classes: low, medium, and high). For each lesion, a series of simulated CT images representing different imaging conditions were synthesized by applying three-dimensional blur and adding correlated noise based on the measured noise and resolution properties of five commercial multislice CT systems, representing three dose levels (
Identifiants
pubmed: 30944842
doi: 10.1117/1.JMI.6.1.013504
pii: 18176R
pmc: PMC6434334
doi:
Types de publication
Journal Article
Langues
eng
Pagination
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