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

Ling Ma, Xiabi Liu, Baowei Fei

Research output: Contribution to journalArticle

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. [Figure not available: see fulltext.].

Original languageEnglish (US)
Pages (from-to)1015-1029
Number of pages15
JournalMedical and Biological Engineering and Computing
Volume58
Issue number5
DOIs
StatePublished - May 1 2020

Keywords

  • Common CT imaging signs of lung diseases (CISL)
  • Lung CT image
  • Medical image retrieval
  • Multi-level
  • Similarity measure

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

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