Texture-Based Analysis of COPD: A Data-Driven Approach

人工智能 模式识别(心理学) 直方图 慢性阻塞性肺病 接收机工作特性 计算机科学 分类器(UML) 数学 医学 图像(数学) 机器学习 精神科
作者
Lauge Sørensen,Mads Nielsen,Pechin Lo,Haseem Ashraf,Jesper Holst Pedersen,Marleen de Bruijne
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:31 (1): 70-78 被引量:83
标识
DOI:10.1109/tmi.2011.2164931
摘要

This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images.The approach uses supervised learning where the class labels are, in contrast to previous work, based on measured lung function instead of on manually annotated regions of interest (ROIs).A quantitative measure of COPD is obtained by fusing COPD probabilities computed in ROIs within the lung fields where the individual ROI probabilities are computed using a k nearest neighbor (kNN) classifier.The distance between two ROIs in the kNN classifier is computed as the textural dissimilarity between the ROIs, where the ROI texture is described by histograms of filter responses from a multi-scale, rotation invariant Gaussian filter bank.The method was trained on 400 images from a lung cancer screening trial and subsequently applied to classify 200 independent images from the same screening trial.The texture-based measure was significantly better at discriminating between subjects with and without COPD than were the two most common quantitative measures of COPD in the literature, which are based on density.The proposed measure achieved an area under the receiver operating characteristic curve (AUC) of 0.713 whereas the best performing density measure achieved an AUC of 0.598.Further, the proposed measure is as reproducible as the density measures, and there were indications that it correlates better with lung function and is less influenced by inspiration level.
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