Analysis of CT images with a pseudo-three dimensional multiscale fuzzy entropy measure: applications to lung diseases

Abstract : Lung diseases are one of the main causes of death worldwide. Identifying their phenotypes and staging their severity can be obtained through computed tomography (CT) images of the pathological site. However, CT can lead to a huge amount of data (many 2D-scans for one region are studied to represent its volume) that can be difficult to analyze and interpret. Therefore, we herein propose a new entropy-based measure, termed pseudo-three dimensional multiscale fuzzy entropy, to quantify the irregularity of high resolution CT scans (HRCT). Data from three groups of subjects are processed: one normal group and two other groups with chronic obstructive pulmonary diseases characterized by a progressive and permanent decline in lung function. The results are interesting for HRCT scan slices, allowing us to further extend this study to a larger number of patients in the future.