Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric
Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric
复制标题
使用纹理特征和学习距离度量进行肺结节分类的基于内容的图像检索
DOI：
10.1007/s10916-017-0874-5
复制
发表时间：
2018-01-01
影响因子：
5.3
通讯作者：
Ma, Zhiqing
中科院分区：
文献类型：
--
作者：
Wei, Guohui;Cao, Hui;Ma, Zhiqing
肺结节的相似性测量是基于内容的图像检索（CBIR）的一个关键组成部分，它可以用于区分计算机断层扫描（CT）上的良性和恶性肺结节。本文提出一种新的肺结节计算机辅助诊断的两步CBIR方案（TSCBIR）。引入语义相关性和视觉相似性两种相似性度量来度量不同结节的相似性。第一步是使用语义相关性度量为每个查询的ROI搜索最相似的参考ROI。第二步是根据检索到的ROI与查询到的ROI的视觉相似性对每个检索到的ROI进行加权。计算概率来预测所查询的ROI描述恶性病变的可能性。为了验证该算法的可行性，从CT扫描的LIDC-IDRI肺图像中组装了包含366个感兴趣结节区域（roi）的肺结节数据集。实现了三组纹理特征来表示结节ROI。我们在组合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，我们的分类器的性能有了很好的提高。
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 forKmost 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.