Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue Elasticity in CT Images.
Affine transformation
Elastic deformation
Invariant characteristics
Lesion classification
Tissue elasticity
Journal
Journal of imaging informatics in medicine
ISSN: 2948-2933
Titre abrégé: J Imaging Inform Med
Pays: Switzerland
ID NLM: 9918663679206676
Informations de publication
Date de publication:
20 Aug 2024
20 Aug 2024
Historique:
received:
06
02
2024
accepted:
14
06
2024
revised:
29
05
2024
medline:
21
8
2024
pubmed:
21
8
2024
entrez:
20
8
2024
Statut:
aheadofprint
Résumé
The elasticity of soft tissues has been widely considered a characteristic property for differentiation of healthy and lesions and, therefore, motivated the development of several elasticity imaging modalities, for example, ultrasound elastography, magnetic resonance elastography, and optical coherence elastography to directly measure the tissue elasticity. This paper proposes an alternative approach of modeling the elasticity for prior knowledge-based extraction of tissue elastic characteristic features for machine learning (ML) lesion classification using computed tomography (CT) imaging modality. The model describes a dynamic non-rigid (or elastic) soft tissue deformation in differential manifold to mimic the tissues' elasticity under wave fluctuation in vivo. Based on the model, a local deformation invariant is formulated using the 1
Identifiants
pubmed: 39164453
doi: 10.1007/s10278-024-01178-8
pii: 10.1007/s10278-024-01178-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIH/NCI
ID : CA206171
Organisme : NIH/NCI
ID : CA220004
Informations de copyright
© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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