CT图像中肺实质的自动分割

目的 为解决肺实质分割中肺部结节及高密度血管易遗漏的问题,提出一种自动肺实质分割方法.方法 首先利用二维区域生长反操作、连通区域判别等方法提取肺实质区域;然后利用行扫描法定位肺区边界点;最后通过对边界点参数分析,定位受肿瘤侵占的边界点,利用曲线拟合修复受损边界.结果 通过对多组胸部CT图像的分割,验证了算法的有效性;与几种常见边界修复算法对比,验证了行扫描边界修复算法的优越性.结论 本文提出的算法能将肿瘤包含到肺实质区域,确保分割的完整性、准确性、实时性.

Auto-segmentation method for lung parenchyma of CT images

Objective To establish a new automatic lung segmentation method in order to deal with the omission of pleural nodules and pulmonary vessels. Methods Lung parenchyma were extracted from chest CT images with the inversed operation of 2D region growing and connected area classification, then the contours and locating the contour points were traced with scan line searching. Finally, the parameters of lung contour points were analyzed to locate the contours distorted by nodules, and curve spline was used to correct distorted contours. Results The experimental results of many sets of CT images verified that the technique proposed was effective. The comparison with other contour correction algorithm verified that line searching contour correction was superior. Conclusion The proposed algorithm includes tumors in the segment results, and confirms the integrality, veracity, real-time quality of this auto-segmentation method.