Uppaluri R, Mitsa T, Sonka M, Hoffman E A, McLennan G
Department of Electrical and Computer Engineering, University of Iowa, Iowa City 52242, USA.
Am J Respir Crit Care Med. 1997 Jul;156(1):248-54. doi: 10.1164/ajrccm.156.1.9606093.
A texture-based adaptive multiple feature method (AMFM) for evaluating pulmonary parenchyma from computed tomography (CT) images is described. This method incorporates multiple statistical and fractal texture features. The AMFM was compared to two previously published methods, namely, mean lung density (MLD) and the lowest fifth percentile of the histogram (HIST). First, the ability of these methods to detect subtle differences in ventral-dorsal lung density gradient in the prone normal lung was studied. Second, their abilities to differentiate between normal and emphysematous whole lung slices were compared. Finally, regional analyses comparing normal and emphysematous regions were performed by dividing the lungs. In the CT slices into six equal regions, ventral to dorsal, and analyzing each region separately. The results demonstrated that the AMFM could separate the ventral from the dorsal one-third of the normal prone lung with 89.8% accuracy, compared to an accuracy of 74.6% with the MLD and 64.4% with the HIST methods. The normal and emphysematous slices were separated on a global basis with 100.0% accuracy using the AMFM as compared to an accuracy of 94.7% and 97.4% using the MLD and HIST methods, respectively. The regional normal and emphysematous tissues were discriminated with an average accuracy of 97.9%, 89.9%, and 99.1% with the AMFM, MLD, and HIST methods, respectively. The three methods and the pulmonary function tests in the normal and emphysema groups were poorly correlated. Quantitative texture analysis using adaptive multiple features holds promise for the objective noninvasive evaluation of the pulmonary parenchyma.
本文描述了一种基于纹理的自适应多特征方法（AMFM），用于从计算机断层扫描（CT）图像中评估肺实质。该方法纳入了多个统计和分形纹理特征。将AMFM与之前发表的两种方法进行了比较，即平均肺密度（MLD）和直方图最低五分位数（HIST）。首先，研究了这些方法检测俯卧位正常肺腹背肺密度梯度细微差异的能力。其次，比较了它们区分正常和肺气肿全肺切片的能力。最后，通过将肺分为六个等份区域（从腹侧到背侧）并分别分析每个区域，对正常和肺气肿区域进行了区域分析比较。结果表明，AMFM能够以89.8%的准确率区分正常俯卧位肺的腹侧和背侧三分之一，相比之下，MLD方法的准确率为74.6%，HIST方法的准确率为64.4%。使用AMFM在整体上区分正常和肺气肿切片的准确率为100.0%，而使用MLD和HIST方法的准确率分别为94.7%和97.4%。AMFM、MLD和HIST方法区分区域正常和肺气肿组织的平均准确率分别为97.9%、89.9%和99.1%。这三种方法与正常组和肺气肿组的肺功能测试相关性较差。使用自适应多特征进行定量纹理分析有望用于肺实质的客观无创评估。
