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Medical & Biological Engineering & Computing
Published in: Medical & Biological Engineering & Computing 5/2020

02-03-2020 | Original Article

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

Authors: Ling Ma, Xiabi Liu, Baowei Fei

Published in: Medical & Biological Engineering & Computing | Issue 5/2020

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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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Metadata
Title
A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases
Authors
Ling Ma
Xiabi Liu
Baowei Fei
Publication date
02-03-2020
Publisher
Springer Berlin Heidelberg
Published in
Medical & Biological Engineering & Computing / Issue 5/2020
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-020-02146-4

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