A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases
A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases
复制标题
肺部疾病常见CT影像征象检索的多级相似性度量
DOI：
10.1007/s11517-020-02146-4
复制
发表时间：
2020-03-02
影响因子：
3.2
通讯作者：
Fei, Baowei
中科院分区：
文献类型：
--
作者：
Ma, Ling;Liu, Xiabi;Fei, Baowei
肺部疾病常见CT征象是肺部CT图像中常见的征象，在肺部疾病的诊断中有着广泛的应用。基于CISL的计算机辅助诊断（CAD）可以提高放射科医师在肺部疾病诊断中的表现。由于相似性度量对CAD的重要性，我们提出了一种多层次的方法来衡量CISL之间的相似性。CISL具有低层视觉尺度、中层属性尺度和高层语义尺度的特征，具有丰富的表示能力。计算多个级别的相似度，并以加权和的形式组合为最终相似度。提出的多层次相似度方法能够计算层次相似度和最优跨层次互补相似度。在511幅临床患者肺部CT图像的数据集上对所提出的相似性度量方法进行了有效性评估。该方法的检索精度可达80%左右，检索时间仅为3.6ms。在相同的数据集上进行了广泛的比较评估，以验证我们的多级相似性度量的检索性能优于单级测量和两级相似性方法。该方法在放射学和决策支持方面具有广泛的应用前景。
The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support.Graphical abstract