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A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

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Abstract

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.

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Funding

This research was supported in part by the National Natural Science Foundation of China, China Postdoctoral Science Foundation (Grant No. 61901234, 2018M641635 to LM), and the Fundamental Research Funds for the Central Universities and partially by the National Natural Science Foundation of China (Grant No. 60973059, 81171407 to XL) and the Program for New Century Excellent Talents in Universities of China (Grant No. NCET-10-0044 to XL).

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Authors and Affiliations

  1. College of Software, Nankai University, Tianjin, People’s Republic of China

    Ling Ma

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, People’s Republic of China

    Xiabi Liu

  3. Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA

    Baowei Fei

  4. Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA

    Baowei Fei

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  1. Ling Ma
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  2. Xiabi Liu
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  3. Baowei Fei
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Correspondence to Xiabi Liu.

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Ma, L., Liu, X. & Fei, B. A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases. Med Biol Eng Comput 58, 1015–1029 (2020). https://doi.org/10.1007/s11517-020-02146-4

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