Park W, Hoffman E A, Sonka M
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, USA.
IEEE Trans Med Imaging. 1998 Aug;17(4):489-97. doi: 10.1109/42.730394.
Three-dimensional (3-D) analysis of airway trees extracted from computed tomography (CT) image data can provide objective information about lung structure and function. However, manual analysis of 3-D lung CT images is tedious, time consuming and, thus, impractical for routine clinical care. We have previously reported an automated rule-based method for extraction of airway trees from 3-D CT images using a priori knowledge about airway-tree anatomy. Although the method's sensitivity was quite good, its specificity suffered from a large number of falsely detected airways. We present a new approach to airway-tree detection based on fuzzy logic that increases the method's specificity without compromising its sensitivity. The method was validated in 32 CT image slices randomly selected from five volumetric canine electron-beam CT data sets. The fuzzy-logic method significantly outperformed the previously reported rule-based method (p < 0.002).
从计算机断层扫描（CT）图像数据中提取的气道树的三维（3-D）分析可以提供有关肺结构和功能的客观信息。然而，对三维肺部CT图像进行人工分析既繁琐又耗时，因此对于常规临床护理来说不切实际。我们之前报道过一种基于规则的自动化方法，利用气道树解剖结构的先验知识从三维CT图像中提取气道树。尽管该方法的灵敏度相当不错，但其特异性却受到大量误检气道的影响。我们提出了一种基于模糊逻辑的气道树检测新方法，该方法在不影响灵敏度的情况下提高了方法的特异性。该方法在从五个犬类容积电子束CT数据集随机选取的32个CT图像切片中得到了验证。模糊逻辑方法明显优于先前报道的基于规则的方法（p < 0.002）。
