Chabat Francois, Yang Guang-Zhong, Hansell David M
Department of Visual Information Processing, Imperial College of Science, Technology and Medicine, Royal Brompton Hospital, Sydney St, London SW3 6NP, England.
Radiology. 2003 Sep;228(3):871-7. doi: 10.1148/radiol.2283020505. Epub 2003 Jul 17.
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.
描述了一种基于薄层计算机断层扫描（CT）图像纹理分析来区分多种阻塞性肺疾病的自动化技术。从每个图像的四个感兴趣区域中提取局部纹理信息，并用一个13维向量表示，该向量包含CT衰减分布的统计矩、采集长度参数和共生描述符。使用监督贝叶斯分类器进行纹理特征分割。该技术在一组患有相似疾病谱的新受试者（n = 33，660个感兴趣区域）中进行了测试。所提出的技术仅基于实质纹理就能很好地区分阻塞性肺疾病的模式。
