目的 针对传统模板匹配方法检测肺结节存在的问题，提出一种用于CT图像中检测肺结节的3维自适应模板匹配算法。方法 首先，从CT序列图像中分割出3维肺实质，采用Canny算子等方法从分割出的3维肺实质中提取3维感兴趣区域作为候选肺结节；然后，确定每个3维感兴趣区域的主方向和中心层，并以此中心层作为信息层，沿主方向对信息层进行3维扩展生成3维模板；最后，对自适应模板和候选结节的3维归一化互相关(NCC)相关系数进行计算，将相似性高于设定阈值的区域标记为肺结节。结果 采用66个临床CT病例对本文方法进行了肺结节检测实验，结果显示本文方法对肺结节检测的敏感率为95.29%，假阳性为12.90%。结论 本文方法对检测肺结节具有较高的敏感率和准确率，可在临床上有效辅助放射科医生对肺结节进行检测，从而提高放射科医生检测肺结节的准确性和工作效率。

Objective Focusing on the traditional template matching Method for nodule detection, a 3D adaptive template matching algorithm for lung nodules detection has been proposed in this paper. Method First of all, 3D lung parenchyma is segmented from the scanned CT images, and then, canny operator is employed for extracting 3D ROI which will be used as the candidate pulmonary nodule. Secondly, collect the main direction of the 3D ROI and locate the center slice, and expand the 3D adaptive template from the center slice trace along the main direction. At last, calculate the correlation coefficient between the 3D adaptive template and pulmonary nodule candidate image by applying Normal Cross Correlation (NCC) algorithm, and set a threshold value of NCC for marking the higher correlation coefficient regions as detection Results. Result Based on 66 clinical cases' experiment, the sensitivity reaches 95.29% and false positive is 12.90%. Conclusion The experiment Results show that our Method has high sensitivity and accuracy and can help radiologists to detect nodules effectively.