title: Obstructive lung diseases: texture classification for differentiation at CT.
abstract: 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 co-occurrence 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.
authors.affiliation: Department of Visual Information Processing, Imperial College of Science, Technology and Medicine, Royal Brompton Hospital, Sydney St, London SW3 6NP, England.
authors.forename: Francois
authors.initials: F
authors.lastname: Chabat
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
authors.forename: David M
authors.initials: DM
authors.lastname: Hansell
mesh_terms: D001989:Bronchiolitis Obliterans / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006801:Humans; D008173:Lung Diseases, Obstructive / Q000145:classification* / Q000000981:diagnostic imaging*; D008433:Mathematics; D011656:Pulmonary Emphysema / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed