Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy. (July 2021)

• Record Type: Journal Article
• Authors: Gaudêncio, Andreia S.; Vaz, Pedro G.; Hilal, Mirvana; Mahé, Guillaume; Lederlin, Mathieu; Humeau-Heurtier, Anne; Cardoso, João M.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Computed tomography -- COVID-19 -- Multiscale entropy -- Texture analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102582
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Pulmonary analysis using 3D multiscale entropy of healthy, IPF, and COVID-19 cases. The 3 groups are statistically different for 9 scale factors ( p < 0.01 ) Complexity index (CI) based on the sum of entropy values for group classification. Threshold and machine-learning classification models for COVID-19 and healthy cases. Abstract: Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 %, a sensitivity of 96.1 %, and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.