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Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric

Journal of Medical Systems volume 42, Article number: 13 (2018) Cite this article

Abstract

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 for K most 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.

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Acknowledgements

The research is supported by the Recruitment Program of Global Experts (Grant no. 01270021814101/022), the National Natural Science Foundation of China (No. 61702087; No. 81671773; No. 61672146; No. 81473708), the Fundamental Research Funds for the Central Universities (Grant no. N150408001).

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Affiliations

  1. School of Science and Engineering, Shandong University of Traditional Chinese medicine, Jinan, 250355, China

    Guohui Wei, Hui Cao & Zhiqing Ma

  2. Sino-Dutch Biomedical and Information Engineering School, Northeast University, Shenyang, China

    He Ma, Shouliang Qi & Wei Qian

  3. Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, USA

    Wei Qian

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  1. Guohui Wei
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  2. Hui Cao
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  3. He Ma
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  4. Shouliang Qi
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  5. Wei Qian
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  6. Zhiqing Ma
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Correspondence to Guohui Wei.

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This article is part of the Topical Collection on Image & Signal Processing

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Wei, G., Cao, H., Ma, H. et al. Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric. J Med Syst 42, 13 (2018). https://doi.org/10.1007/s10916-017-0874-5

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  • DOI: https://doi.org/10.1007/s10916-017-0874-5

Keywords

  • Content-based image retrieval
  • Texture feature
  • Similarity metric
  • Computer-aided diagnosis
  • Lung nodule