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Radiology. 2003 Sep;228(3):871-7. Epub 2003 Jul 17.

Obstructive lung diseases: texture classification for differentiation at CT.

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Department of Visual Information Processing, Imperial College of Science, Technology and Medicine, Royal Brompton Hospital, Sydney St, London SW3 6NP, England.


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.

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