Sørensen Lauge, Lo Pechin, Dirksen Asger, Petersen Jens, de Bruijne Marleen
The Image Group, Department of Computer Science, University of Copenhagen, Denmark.
Inf Process Med Imaging. 2011;22:475-85. doi: 10.1007/978-3-642-22092-0_39.
A novel method for classification of abnormality in anatomical tree structures is presented. A tree is classified based on direct comparisons with other trees in a dissimilarity-based classification scheme. The pair-wise dissimilarity measure between two trees is based on a linear assignment between the branch feature vectors representing those trees. Hereby, localized information in the branches is collectively used in classification and variations in feature values across the tree are taken into account. An approximate anatomical correspondence between matched branches can be achieved by including anatomical features in the branch feature vectors. The proposed approach is applied to classify airway trees in computed tomography images of subjects with and without chronic obstructive pulmonary disease (COPD). Using the wall area percentage (WA%), a common measure of airway abnormality in COPD, as well as anatomical features to characterize each branch, an area under the receiver operating characteristic curve of 0.912 is achieved. This is significantly better than computing the average WA%.
提出了一种用于解剖树结构异常分类的新方法。在基于差异的分类方案中，通过与其他树进行直接比较来对树进行分类。两棵树之间的成对差异度量基于表示这些树的分支特征向量之间的线性分配。据此，分支中的局部信息被共同用于分类，并考虑了整棵树特征值的变化。通过在分支特征向量中纳入解剖特征，可以实现匹配分支之间的近似解剖对应。将所提出的方法应用于对患有和未患有慢性阻塞性肺疾病（COPD）的受试者的计算机断层扫描图像中的气道树进行分类。使用壁面积百分比（WA%）（COPD中气道异常的常用度量）以及表征每个分支的解剖特征，获得了受试者工作特征曲线下面积为0.912。这明显优于计算平均WA%。
