基于区域增长的肺结节自适应形态分割  

Self-adapted segmentation of pulmonary nodule based on region growing

作　　者：曹蕾[1] 路利军[1] 杨蕊梦[2] 陈武凡[1] 

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]南方医科大学南方医院影像中心,广东广州510515

出　　处：《南方医科大学学报》2008年第12期2109-2112,2125,共5页Journal of Southern Medical University

基　　金：国家“973”重点基础研究发展规划项目(2003CB716104);国家自然科学基金重点项目(30730036);广东省科技计划项目(2007B010400058);广州市科技计划项目(2007Z3-E0031)

摘　　要：目的提高计算机辅助诊断系统对胸部CT图像中肺结节,特别是与胸膜相连和与血管相连的肺结节分割的准确性。方法首先提出了肺结节图像的自动分割过程。然后,应用基于对比度和梯度的区域增长方法,获得肺结节的分割图像。最后,针对区域增长法不能成功分割的特殊情况,提出了自适应形态分割算法。结果对临床2D肺部CT图像的初步实验结果表明,应用本算法能够成功的将孤立性肺结节和与胸膜或血管相连的肺结节分割出来。结论这是一种实用的肺结节自动分割算法。Objective To improve the accuracy and efficiency of pulmonary nodule segmentation of thoracic CT image for computer-aided diagnostic （CAD） system, especially for those nodules adhering to the pleural or blood vessels. Methods We proposed the automatic process of pulmonary nodule segmentation, and using region growing method based on the contrast and gradient, the pulmonary nodule images were acquired. A self-adapted morphologic segmentation algorithm was presented for the unsuccessful nodule segmentation using region growing. Results and Conclusion Experiments on clinical 2D pulmonary images showed that the solitary pulmonary nodules and those adhering to the pleural or blood vessels could all be segmented. This pulmonary nodule segmentation algorithm is feasible for automated segmentation of the thoracic CT images.

关 键 词：肺结节分割 肺部CT 区域增长 计算机辅助诊断系统 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]