Accurate airway segmentation based on intensity structure analysis and graph-cut
Accurate airway segmentation based on intensity structure analysis and graph-cut
复制标题
基于强度结构分析和图割的精确气道分割
DOI：
10.1117/12.2216670
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发表时间：
2016
影响因子：
10.6
通讯作者：
K. Mori
中科院分区：
文献类型：
--
作者：
Qier Meng;T. Kitasaka;Y. Nimura;M. Oda;K. Mori
本文提出了一种基于强度结构分析和图割的新型气道分割方法。气道分割是分析胸部 CT 体积以进行计算机化肺癌检测、肺气肿诊断、哮喘诊断以及术前和术中支气管镜导航的重要步骤。然而，从 CT 体积中获取完整的 3D 气道树结构非常具有挑战性。一些研究人员提出了基本上基于区域生长和机器学习技术的自动化算法。然而这些方法未能检测到外周支气管分支。它们造成了大量泄漏。本文提出了一种新方法，可以更准确地提取复杂的支气管气道区域。我们的方法由三个步骤组成。首先，利用Hessian分析增强CT体积中的线状结构，然后采用多尺度空腔增强滤波器从先前的增强结果中检测出空腔结构。第二步，我们利用支持向量机（SVM）构建一个分类器来去除生成的 FP 区域。最后，利用图割算法连接所有候选体素以形成完整的气道树。我们将此方法应用于 16 例 3D 胸部 CT 体积。结果表明，该方法的分支检出率可达77.7%左右，且不会漏入肺实质区域。
This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the lung parenchyma areas.