Open AccessSupport System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images
Author(s) -
AUTHOR_ID,
D. E. Rodríguez-Obregón
Publication year - 2021
Language(s) - English
Resource type - Dissertations/theses
DOI - 10.17488/rmib.381.11
Subject(s) - clinical practice , artificial intelligence , segmentation , fibrosis , field (mathematics) , computed tomography , computer science , pulmonary disease , pulmonary fibrosis , medicine , pattern recognition (psychology) , computer vision , radiology , mathematics , pathology , family medicine , pure mathematics
A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis region with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitativeestimation of fibrosis region.