Skip to main content

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

Abstract

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy “1”,“2” as benign and “4”,“5” as malignant), configuration-2 (composite rank of malignancy “1”,“2”, “3” as benign and “4”,“5” as malignant), and configuration-3 (composite rank of malignancy “1”,“2” as benign and “3”,“4”,“5” as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Siegel R, Naishadham D, Jemal A. Cancer statistics. CA Cancer J Clin 2013;63(1):11–30.

    Article  PubMed  Google Scholar 

  2. 2.

    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 2002;222(3):773–781.

    Article  PubMed  Google Scholar 

  3. 3.

    Ko JP, Naidich DP. Computer-aided diagnosis and the evaluation of lung disease. J Thorac Imaging 2004; 19(3):136–155.

    Article  PubMed  Google Scholar 

  4. 4.

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

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Seitz Jr KA, Giuca AM, Furst J, Raicu D. Learning lung nodule similarity using a genetic algorithm. Proceedings of SPIE Medical Imaging 2012. San Deigo, USA; 2012. p. 831,537–831,537–7.

  6. 6.

    Dhara AK, Mukhopadhyay S, Das Gupta R, Garg M, Khandelwal N. Erratum to: a segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 2016;29(1):148–148.

    Article  PubMed  Google Scholar 

  7. 7.

    Tourassi GD, Vargas-Voracek R, Floyd Jr CE. Content-based image retrieval as a computer aid for the detection of mammographic masses. SPIE Medical Imaging 2003; 2003. p. 590–597.

  8. 8.

    Jin R, Meng B, Song E, Xu X, Jiang L. Computer-aided detection of mammographic masses based on content-based image retrieval. SPIE Medical Imaging 2007; 2007. p. 65,141w–65,141w.

  9. 9.

    Jiang M, Zhang S, Li H, Metaxas DN. Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans Biomed Eng 2015;62(2):783–792.

    Article  PubMed  Google Scholar 

  10. 10.

    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 2004;73(1):1–23.

    Article  PubMed  Google Scholar 

  11. 11.

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

  12. 12.

    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. Comput Vis Image Underst 1999;75(2):111–132.

    Article  Google Scholar 

  13. 13.

    Florea F, Müller H, Rogozan A, Geissbuhler A, Darmoni S. Medical image categorization with MediC and MedGIFT. Netherlands: Maastricht; 2006, pp. 3–11.

    Google Scholar 

  14. 14.

    Kelly PM, Cannon TM, Hush DR. Query by image example: the comparison algorithm for navigating digital image databases (candid) approach. IS&T/SPIE’s Symposium on Electronic Imaging: Science & Technology; 1995. p. 238–248.

  15. 15.

    Müller H, Lovis C, Geissbuhler A. The MedGIFT project on medical image retrieval. Medical Imaging and Telemedicine 2005;2.

  16. 16.

    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 Trans Med Imaging 2006;25(4):417–434.

    Article  PubMed  Google Scholar 

  17. 17.

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

  18. 18.

    Kubota T, Jerebko AK, Dewan M, Salganicoff M, Krishnan A. Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal 2011;15(1):133–154.

    Article  PubMed  Google Scholar 

  19. 19.

    Silva JS, Santos JB, Roxo D, Martins P, Castela E, Martins R. Algorithm versus physicians variability evaluation in the cardiac chambers extraction. IEEE Trans Inf Technol Biomed 2012;16(5):835–841.

    Article  PubMed  Google Scholar 

  20. 20.

    Kligerman S, White C. Imaging characteristics of lung cancer. Semin Roentgenol 2011;46(3):194–207.

    Article  PubMed  Google Scholar 

  21. 21.

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

    Article  Google Scholar 

  22. 22.

    Lorensen WE, Cline HE. Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Computer Graphics 1987;21(4):163–169.

    Article  Google Scholar 

  23. 23.

    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 Comput Assist Radiol Surg 2016;11(3):337–349 .

    Article  PubMed  Google Scholar 

  24. 24.

    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 2016;10(9):631–637.

    Article  Google Scholar 

  25. 25.

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

    CAS  Article  PubMed  Google Scholar 

  26. 26.

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

  27. 27.

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

    Article  Google Scholar 

  28. 28.

    Dalal N, Triggs B, Schmid C. Human detection using oriented histograms of flow and appearance. Computer Vision–ECCV 2006. Springer; 2006. p. 428–441.

  29. 29.

    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 2014;28(1):99–115.

    Article  PubMed Central  Google Scholar 

  30. 30.

    Noessner J, Niepert M, Stuckenschmidt H. 2013. ROCKIT: Exploiting parallelism and symmetry for map inference in statistical relational models. arXiv:1304.4379.

  31. 31.

    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 2005;27(8):1226–1238.

    Article  PubMed  Google Scholar 

  32. 32.

    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 2011;38 (2):915–931.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Dasovich GM, Kim R, Raicu DS, Furst JD. A model for the relationship between semantic and content based similarity using LIDC. Proceedings of SPIE Medical Imaging 2010. San Diego, USA; 2010. p. 762,431–762,431–10.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sudipta Mukhopadhyay.

Ethics declarations

Conflict of interests

This study was funded by the Department of Electronics and Information Technology, Govt. of India, Grant number 1(2)/2013-ME &TMD/ESDA. The authors declare that they have no conflict of interest. This work is done using a public lung CT image data set and for this type of study formal consent is not required. This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dhara, A.K., Mukhopadhyay, S., Dutta, A. et al. Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer. J Digit Imaging 30, 63–77 (2017). https://doi.org/10.1007/s10278-016-9904-y

Download citation

Keywords

  • CT images
  • Content-based image retrieval
  • Diagnosis of lung cancer
  • Lung cancer
  • Pulmonary nodules
  • Self-learning tool of radiology
  • CBIR based CAD system