结合区域生长与灰度重建的CT图像肺气管树分割  

Segmentation of pulmonary airway tree in CT images by combining region growing and grayscale reconstruction algorithms

作　　者：彭双[1] 肖昌炎[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《中国图象图形学报》2014年第9期1377-1383,共7页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61172160);湖南省自然科学常德联合基金项目(612JJ9019)

摘　　要：目的 CT图像中肺气管树分割对肺部疾病的精确定位与量化评估具有重要意义。针对不同气管分枝在大小、形状和密度分布方面的差异,提出一种区域生长与形态学灰度重建相结合的3维肺气管树分割流程,重点解决气管提取过程中的局部泄漏问题。方法首先,采用阈值分割和形态学闭运算提取肺实质以定义感兴趣区域;然后,通过改进迟滞阈值区域生长法分割较粗气管,结合局部体积突变指标抑制侧向泄漏;接着,利用3维形态学灰度重建算法分割较细气管,并采用形状约束连接元分析和管形描述子剔除伪气管区域;最后,将上述两步分割结果融合成完整气管树。结果采用EXACT'09竞赛提供的公开数据,选取20例测试图像及手工标记结果作为参考,分别从分杈点、分支数量和分支数比率等方面进行量化评估。实验结果表明,本文方法能在较低泄漏前提下成功检测出参考标准中一半以上的分支、平均分支数比率达到55.5%。结论与竞赛网站公布的其他方法相比,本文方法结果评价指标处于中上水平,但算法简单、计算复杂度低、易于实现,在泄漏检测方面有一定优势。Objective The segmentation of pulmonary airway tree serves an important function in the accurate localization and quantitative evaluation of lung diseases.Considering the size,shape,and density variations among different bronchus branches,we propose a method for 3D pulmonary airway tree segmentation by combining region growing and morphological grayscale reconstruction algorithms with a focus on solvinglocal leakage problems.Method First,the region of interestor maskis defined by extracting the lung parenchyma with a global threshold and morphological closingoperation.The trachea and large bronchi are then segmented by using an improved region growing method based on the iterative hysteresis threshold,and a local volume explosion index is adopted to suppress the lateral leakage.The smaller bronchi are segmented using a 3 D morphological grayscale reconstruction algorithm,and a connected component-based shape descriptor is defined to remove the pseudo-tracheal regions.Finally,a complete airway tree is obtained by integrating the segmentation results from the initial two steps.Result The algorithms are tested by using the publicly available data of the EXACT09 challenge,and quantitative evaluation is conducted with the bifurcation number,branch number,and branch ratio o fthe 20 test CT cases by comparing the details with a manual reference.Results show that the proposed method can identify more than half of the branches in the reference,and the mean number of detected branches can reach 55.5％ under a relatively low leakage rate.Conclusion Compared with that of other considered methods,the performance indexes of the proposed method are moderately better.Although the algorithm appears to be simple and has low computational complexity,the proposed method effectively prevents leakage.

关 键 词：气管树分割 区域生长 形态学重建 CT图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]