BACKGROUND Automatic segmentation of pulmonary airway tree is a challenging task in many clinical applications, including developing computer-aided detection and diagnosis schemes of lung diseases. OBJECTIVE To segment the pulmonary airway tree from the computed tomography (CT) chest images using a novel automatic method proposed in this study. METHODS This method combines a two-pass region growing algorithm with gray-scale morphological reconstruction and leakage elimination. The first-pass region growing is implemented to obtain a rough airway tree. The second-pass region growing and gray-scale morphological reconstruction are used to detect the distal airways. Finally, leakage detection is performed to remove leakage and refine the airway tree. RESULTS Our methods were compared with the gold standards. Forty-five clinical CT lung image scan cases were used in the experiments. Statistics on tree division order, branch number, and airway length were adopted for evaluation. The proposed method detected up to 12 generations of bronchi. On average, 148.85 branches were extracted with a false positive rate of 0.75%. CONCLUSIONS The results show that our method is accurate for pulmonary airway tree segmentation. The strategy of separating the leakage detection from the segmenting process is feasible and promising for ensuring a high branch detected rate with a low leakage volume.

中文翻译：

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结合形态学和骨架分析的区域增长算法分割CT图像中的气道树。

背景技术在许多临床应用中，包括开发计算机辅助的肺部疾病检测和诊断方案，肺气道树的自动分割是一项艰巨的任务。目的使用本研究提出的一种新颖的自动方法从计算机断层扫描（CT）胸部图像中分割肺气道树。方法该方法将两遍区域生长算法与灰度形态重建和泄漏消除相结合。实施第一遍区域生长以获得粗糙的气道树。第二遍区域生长和灰度形态重建用于检测远端气道。最后，执行泄漏检测以消除泄漏并优化气道树。结果我们的方法与金标准进行了比较。实验中使用了45例临床CT肺部图像扫描病例。评估采用树的划分顺序，分支数和气道长度的统计数据。所提出的方法可检测多达12代支气管。平均而言，提取出148.85个分支，假阳性率为0.75％。结论结果表明我们的方法对于肺气道树分割是准确的。将泄漏检测与分段过程分离的策略是可行的，并有望确保以低泄漏量实现高分支检测率。结论结果表明我们的方法对于肺气道树分割是准确的。将泄漏检测与分段过程分离的策略是可行的，并有望确保以低泄漏量实现高分支检测率。结论结果表明我们的方法对于肺气道树分割是准确的。将泄漏检测与分段过程分离的策略是可行的，并有望确保以低泄漏量实现高分支检测率。