基于密集特征匹配的胸片肺野自动分割

Automatic segmentation of lung fields in chest radiographs based on dense matching of local features

目的 X线胸片中肺野的准确分割是胸片图像自动分析的必要步骤。本文采用局部特征的密集匹配和标号融合进行胸片肺野的自动分割。方法对于输入的待分割胸片,基于每个像素点提取密集SIFT描述子和图像块作为局部特征,采用密集匹配直接在整个参考图像特征集合中快速搜索近邻;密集匹配分为受限的随机初始化、近邻场传播和受限的随机搜索三步,并数次迭代后两步。利用匹配得到的近邻场,提取标号图像块并进行加权,权重为匹配的相似度,最后重组为肺野的概率图,经阈值化处理即可得到肺野的分割。结果在公开的JSRT胸片图像数据集上进行测试,本文方法的Jaccard指标可达95.5%。结论利用局部特征的密集匹配和标号融合能取得准确性高且稳定的胸片肺野分割效果,并且优于当前最好的胸片肺野分割方法。

Objective Accurate segmentation of lung fields in chest radiographs（CXR） is very useful for automatic analysis of CXR. In this work, we propose to use dense matching of local features and label fusion to automatically segment the lung fields in CXR. Methods For an input CXR, the dense Scale Invariant Feature Transform（SIFT） descriptors and raw image patches were extracted as the local features for each pixel. The nearest neighbors of the local features were then quickly searched by dense matching directly from the whole feature dataset of the reference images. The dense matching included three steps： limited random initialization, propagation of nearest neighbor field, and limited random search, with iteration of the last two steps for several times. The label image patches for each pixel were extracted according to the nearest neighbor field and weighted by the matching similarity. Finally, the weighted label patches were rearranged as the label class probability image of the input CXR, from which thresholds were obtained for segmentation of the lung fields. Results The Jaccard index of the proposed method reached 95.5% on the public JSRT dataset. Conclusion A high accuracy and robustness can be obtained by adopting dense matching of local features and label fusion to segment the lung fields in CXR, and the result is better than that of current segmentation method.