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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.

References

  1. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Clarke LP Data from LIDC-IDRI. The Cancer Imaging Archive. 2015. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

  2. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DPY, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP: The lung image database consortium (LIDC,) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931, 2011

  3. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, et al: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057, 2013

  4. Dalal N, Triggs B, Schmid C: Human detection using oriented histograms of flow and appearance.. In: Computer vision–ECCV 2006, pp. 428–441. Springer, 2006

  5. Dash JK, Mukhopadhyay S, Gupta RD: Content-based image retrieval using fuzzy class membership and rules based on classifier confidence. IET Image Process 9(9):836–848, 2015

  6. Dash JK, Mukhopadhyay S, Khandelwal N: Complementary cumulative precision distribution: a new graphical metric for medical image retrieval system.. In: SPIE Medical imaging, pp 90,371s–90,371s. International society for optics and photonics, 2014

  7. Dhara A, Mukhopadhyay S, Das Gupta R, Garg M, Khandelwal N: A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 10:1007, 2015

  8. Dhara AK, Mukhopadhyay S, Chakrabarty S, Garg M, Khandelwal N: Quantitative evaluation of margin sharpness of pulmonary nodules in lung CT images. IET Image Process 10(9):631–637, 2016

  9. Dhara AK, Mukhopadhyay S, Dutta A, Garg M, Khandelwal N: Content-based image retrieval system for pulmonary nodules: Assisting radiologists in self-learning and diagnosis of lung cancer. J Digit Imaging 30(1):63–77, 2017

  10. Dhara AK, Mukhopadhyay S, Saha P, Garg M, Khandelwal N: Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images. Int J CARS 11(3):337–349, 2016

  11. Diederich S, Wormanns D, Semik M, Thomas M, Lenzen H, Roos N, Heindel W: Screening for early lung cancer with low-dose spiral CT: Prevalence in 817 asymptomatic smokers. Radiology 222(3):773–781, 2002

  12. Han F, Wang H, Zhang G, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z: Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging 28(1):99–115, 2014

  13. Haralick RM, Shanmugam K, Dinstein IH: Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621, 1973

  14. Kelly P, Cannon T, Hush D: Query by image example: the comparison algorithm for navigating image databases (CANDID) approach.. In: Proceedings of the SPIE, 1995

  15. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, Peitgen HO: Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic ct scans. IEEE Transactions on Medical Imaging 25 (4): 417–434, 2006

  16. Lam MO, Disney T, Raicu DS, Furst J, Channin DS: BRISC − an open source pulmonary nodule image retrieval framework. J Digit Imaging 20(1):63–71, 2007

  17. Lehmann TM, Schubert H, Keysers D, Kohnen M, Wein BB: The IRMA code for unique classification of medical images.. In: Proceedings of SPIE Medical Imaging 2003, pp 440–451, 2003

  18. Li Z, Ma L, Jin X, Zheng Z: A new feature-preserving mesh-smoothing algorithm. Vis Comput 25(2):139–148, 2009

  19. Lorensen WE, Cline HE: Marching cubes: a high resolution 3d surface construction algorithm.. In: ACM Siggraph computer graphics, vol 21, pp 163–169. ACM, 1987

  20. Ma WY, Manjunath BS: Texture features and learning similarity.. In: IEEE Computer society conference on computer vision and pattern recognition, pp 425–430, 1996

  21. Mishra S, Joseph RA, Gupta PC, Pezzack B, Ram F, Sinha DN, Dikshit R, Patra J, Jha P: Trends in bidi and cigarette smoking in India from 1998 to 2015, by age, gender and education. BMJ Global Health 1(1):e000,005, 2016

  22. Moltz JH, Kuhnigk JM, Bornemann L, Peitgen H: Segmentation of juxtapleural lung nodules in CT scan based on ellipsoid approximation.. In: Proceedings of First International Workshop on Pulmonary Image Processing. New York, pp 25–32, 2008

  23. Mukhopadhyay S, Dash JK, Gupta RD: Content-based texture image retrieval using fuzzy class membership. Pattern Recogn Lett 34(6):646–654, 2013

  24. Müller H, Lovis C, Geissbuhler A: The MedGIFT project on medical image retrieval. Medical Imaging and Telemedicine, Wujishan, China, 2005

  25. Müller H., Michous N, Bandon D, Geissbuhler A: A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int J Med Inform 73(1):1–23, 2004

  26. Peng H, Long F, Ding C: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238, 2005

  27. Perona P, Malik J: Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639, 1990

  28. Rangayyan RM, El-Faramawy NM, Desautels JL, Alim OA: Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imaging 16(6):799–810, 1997

  29. Seitz KA Jr, Giuca AM, Furst J, Raicu D: Learning lung nodule similarity using a genetic algorithm.. In: Proceedings of SPIE Medical Imaging 2012, pp 831537. San Deigo, USA, 2012

  30. Shyu C, Brodley CE, Kak AC, Kosaka A, Aisen A: Broderick, l.: ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases. Comp Vision Image Underst 75(2):111–132, 1999

  31. Siegel R, Jemal A (2015) Cancer facts & figures 2015. American Cancer Society Cancer Facts & Figures

  32. Sladoje N, Nyström I, Saha PK: Measurements of digitized objects with fuzzy borders in 2D and 3D. Image Vis Comput 23(2):123–132, 2005

  33. Tripathi AK, Mukhopadhyay S, Dhara AK: Performance metrics for image contrast.. In: Proceedings of IEEE International Conference on Image Information Processing, pp 1–4. Simla, India, 2011

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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

Authors
  1. Shrikant A. Mehre
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  2. Ashis Kumar Dhara
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  6. Sudipta Mukhopadhyay
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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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