Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19.

ABSTRACT:

Background

The accurate separation of lung parenchyma, ground-glass opacity (GGO), and intrapulmonary vessels on high-resolution computed tomography (HRCT) in coronavirus disease 2019 (COVID-19) is challenging.

Methods

We conducted a cross-sectional study that analyzed 530 adults (20-40 years) with RT-PCR-confirmed COVID-19. For texture modeling, we sampled 597 regions of interest (ROIs) representing parenchyma, GGO, and intrapulmonary vessels. Region-of-interest-labeled HRCT patches representing parenchyma, GGO, and vessels were analyzed using first- and second-order texture features that were computed across different square window sizes (5 × 5-20 × 20 pixels). Feature selection with stepwise linear discriminant analysis yielded a three-class classifier. The primary endpoi

SUBMITTER: Hassib MA 

PROVIDER: S-EPMC12714659 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature