Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3910-3913. doi: 10.1109/EMBC.2017.8037711.
Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules. Each nodule is represented by a vector of 26 texture features. We then propose a content-based image retrieval (CBIR) scheme to classify between benign and malignant lung nodules with a learned Mahalanobis distance metric. The Mahalanobis distance metric as a similarity metric can preserve semantic relevance and visual similarity of lung nodules. The CBIR approach uses this Mahalanobis distance to search for most similar reference nodules for each queried nodule. The majority of votes are then computed to predict the likelihood of the queried nodule depicting a malignant lesion. For the classification accuracy, the area under the ROC curve (AUC) can achieve as 0.942±0.008. The recall and precision of benign nodules are 0.860 and 0.889, respectively. The recall and precision of malignant nodules are 0.893 and 0.866, respectively.
肺结节的相似性度量有助于在计算机断层扫描（CT）上区分良性和恶性肺结节病变。与以往侧重于特征提取的计算机化方案不同，我们专注于肺结节的相似性度量。在本研究中，我们首先收集了一个来自LIDC-IDRI肺部CT图像的肺结节数据集。该数据集包括746个肺结节，其中375个由领域放射科医生识别为恶性结节，371个由领域放射科医生识别为良性结节。每个结节由一个包含26个纹理特征的向量表示。然后，我们提出了一种基于内容的图像检索（CBIR）方案，使用学习到的马氏距离度量对良性和恶性肺结节进行分类。马氏距离度量作为一种相似性度量，可以保留肺结节的语义相关性和视觉相似性。CBIR方法使用这种马氏距离为每个查询结节搜索最相似的参考结节。然后计算多数投票来预测查询结节呈现恶性病变的可能性。对于分类准确率，ROC曲线下面积（AUC）可达0.942±0.008。良性结节的召回率和精确率分别为0.860和0.889。恶性结节的召回率和精确率分别为0.893和0.866。
