School of Science and Engineering, Shandong University of Traditional Chinese medicine, Jinan, 250355, China.
Sino-Dutch Biomedical and Information Engineering School, Northeast University, Shenyang, China.
J Med Syst. 2017 Nov 29;42(1):13. doi: 10.1007/s10916-017-0874-5.
Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric. The second step is to weight each retrieved ROI based on its visual similarity to the queried ROI. The probability is computed to predict the likelihood of the queried ROI depicting a malignant lesion. In order to verify the feasibility of the proposed algorithm, a lung nodule dataset including 366 nodule regions of interest (ROIs) is assembled from LIDC-IDRI lung images on CT scans. Three groups of texture features are implemented to represent a nodule ROI. Our experimental results on the assembled lung nodule dataset show good performance improvement over existing popular classifiers.
肺结节的相似性度量是基于内容的图像检索（CBIR）中的一个关键组成部分，它可以在计算机断层扫描（CT）上区分良性和恶性肺结节方面发挥作用。本文提出了一种新的两步 CBIR 方案（TSCBIR），用于肺结节的计算机辅助诊断。引入了两种相似性度量，语义相关性和视觉相似性，以度量不同结节之间的相似性。第一步是使用语义相关性度量为每个查询 ROI 搜索 K 个最相似的参考 ROI。第二步是根据检索到的 ROI 与查询 ROI 的视觉相似性对其进行加权。计算概率以预测查询 ROI 描绘恶性病变的可能性。为了验证所提出算法的可行性，从 CT 扫描的 LIDC-IDRI 肺部图像中组装了一个包含 366 个结节感兴趣区域（ROI）的肺结节数据集。实现了三组纹理特征来表示一个结节 ROI。我们在组装的肺结节数据集上的实验结果表明，与现有的流行分类器相比，性能有了很好的提高。
