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Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

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Abstract

Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured.

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Notes

  1. Data Citation: Armato III, Samuel G., McLennan, Geoffrey, Bidaut, Luc, McNitt-Gray, Michael F., Meyer, Charles R., Reeves, Anthony P., Clarke, Laurence P. (2015). Data From LIDC-IDRI. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

    Publication Citation: Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915–931, 2011.

    TCIA Citation: Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057.

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Acknowledgements

The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.

Funding

This study was funded by Ministry of Electronics and Information Technology, Government of India (grant no.: 1(2)/2013-ME&TMD/ESDA).

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Authors and Affiliations

  1. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Shrikant A. Mehre, Ashis Kumar Dhara & Sudipta Mukhopadhyay

  2. Centre for Image Analysis, Uppsala University, Uppsala, Sweden

    Ashis Kumar Dhara

  3. Department of Radiodiagnosis and Imaging, Post-graduate Institute of Medical Education and Research, Chandigarh, India

    Mandeep Garg, Naveen Kalra & Niranjan Khandelwal

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  1. Shrikant A. Mehre
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Corresponding author

Correspondence to Sudipta Mukhopadhyay.

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The authors declare that they have no conflict of interest.

Ethical Approval

All procedures in this study involving human participants were performed in accordance with the ethical standards of the institution, and were approved by the research ethics boards at Indian Institute of Technology Kharagpur and Postgraduate Institute of Medical Education and Research, Chandigarh. This study does not contain any procedures involving animals.

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Informed consent was obtained from all individual participants included in the study.

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Mehre, S.A., Dhara, A.K., Garg, M. et al. Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval. J Digit Imaging 32, 362–385 (2019). https://doi.org/10.1007/s10278-018-0136-1

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