An automated technique for differentiation between a variety of obstructive lung diseases on the basis of textural analysis of thin-section computed tomographic (CT) images is described. From four regions of interest on each image, local texture information was extracted and represented by a 13-dimensional vector that contained statistical moments of the CT attenuation distribution, acquisition-length parameters, and cooccurrence descriptors. A supervised Bayesian classifier was used for texture feature segmentation. The technique was tested with a new cohort of subjects (n = 33, 660 regions of interest) with a similar spectrum of diseases. The proposed technique discriminates well between patterns of obstructive lung disease on the basis of parenchymal texture alone.
Thin-section computed tomography (CT) is an accurate imaging technique for the detection of various obstructive lung diseases, including centrilobular emphysema and constrictive bronchiolitis. Features on thin-section CT images can be subtle, particularly in the early stages of disease, and diagnosis is subject to inter-observer variation. The main image characteristic used for the detection of obstructive lung diseases is the presence of areas of abnormally low attenuation in the lung parenchyma, which, in the case of emphysematous destruction of the lung parenchyma, can be detected automatically by means of attenuation masking (1,2). However, areas of decreased attenuation are a feature of other obstructive lung diseases; thus, identification does not always permit a confident diagnosis. To refine the differential diagnosis of obstructive lung disease, it is necessary to take into account the textural appearance of lung parenchyma with abnormally low attenuation. The aim of this study was to describe and test an automated method for the differentiation of centrilobular emphysema, pan-lobular emphysema, constrictive obliter-ative bronchiolitis, and normal lung tissue on the basis of texture features.