计算机工程与应用2016，Vol.52Issue(13)：201-205,5.DOI:10.3778/j.issn.1002-8331.1407-0623
结合区域生长与模糊连接度的肺气管树分割
Segmentation of pulmonary airway tree by combining region growing and fuzzy connectedness
摘要
Abstract
Considering the noise, intensity inhomogeneity and boundary fuzzy in CT image, it is difficult to segment pul-monary airway tree accurately. To improve the segmentation results, 3D multi-seeded fuzzy connectedness algorithm is proposed. Firstly, the region of interest is defined by extracting the lung parenchyma with a global threshold and a mor-phological closing operation. Secondly, the trachea and big bronchi are pre-segmented using an improved region growing method on basis of an iterative hysteresis threshold, and a local volume explosion index is adopted to suppress the lateral leakage. Then, branch points are extracted from the skeleton which is extracted and pruned from the pre-segmentation results, and these points as the new seed points for subsequent segmentation. Finally, the fuzzy connectedness between the seed and any point is calculated by constructing affinity function according to intensity homogeneity and tubular structure characteristics of the trachea. Besides, the fuzzy connectedness is segmented by choosing an appropriate threshold. The algorithms are tested with the publicly available data of the EXACT'09 challenge, and the quantitative evaluation is con-ducted with the node number, branch number and branch ratio on the 20 test CT cases by comparing with a manual refer-ence. The proposed method is able to detect more than half of branches in the reference and the mean numbers of detected branches reach 59.7% under a relatively low leakage rate. The experimental results show that the method has more accu-rate segmentation results.关键词
气管树分割/区域生长/模糊连接度/CT图像Key words
airway tree segmentation/region growing/fuzzy connectedness/CT image分类
计算机与自动化引用本文复制引用
彭双,肖昌炎..结合区域生长与模糊连接度的肺气管树分割[J].计算机工程与应用,2016,52(13):201-205,5.基金项目
国家自然科学基金(No.61172160) （No.61172160）
湖南省自然科学常德联合基金(No.12JJ9019). （No.12JJ9019）
