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3D shape analysis to reduce false positives for lung nodule detection systems

Medical & Biological Engineering & Computing volume 55, pages 1199–1213 (2017)Cite this article

Abstract

Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC–IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC–IDRI database, and cross-validation with k-fold, where \(k = 5\), was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.

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References

  1. Akram S, Javed MY, Hussain A, Riaz F, Akram MU (2015) Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques. J Exp Theor Artif Intell 27(6):737–751. doi:10.1080/0952813X.2015.1020526

    Article  Google Scholar 

  2. Al-Absi H, Samir B, Shaban K, Sulaiman S (2012) Computer aided diagnosis system based on machine learning techniques for lung cancer. In: 2012 international conference on computer information science (ICCIS), 1:295–300. doi:10.1109/ICCISci.2012.6297257

  3. Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke 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, Batrah 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, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY (2011) 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. http://www.biomedsearch.com/nih/Lung-Image-Database-Consortium-LIDC/21452728.html

  4. de Carvalho Filho AO, de Sampaio WB, Silva AC, de Paiva AC, Nunes RA, Gattass M (2013) Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif Intell Med. doi:10.1016/j.artmed.2013.11.002. http://www.sciencedirect.com/science/article/pii/S0933365713001541

  5. Chen W, Li Z, Bai L, Lin Y (2011) Nf-kappab in lung cancer, a carcinogenesis mediator and a prevention and therapy target. Front Biosci (Landmark Ed) 16:1172–85. doi:10.2741/3782

    CAS  Article  Google Scholar 

  6. Duda RO, Hart PE (1973) Pattern Classification and Scene Analysis. Wiley-Interscience Publication, New York

    Google Scholar 

  7. van Erkel A, Pattynama P (1998) Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 27(2):88–94

    Article  PubMed  Google Scholar 

  8. Farag A, Ali A, Graham J, Farag A, Elshazly S, Falk R (2011) Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest. In: IEEE international symposium on biomedical imaging: from nano to macro, pp. 169–172. doi:10.1109/ISBI.2011.5872380

  9. Fujimoto J, Wistuba II (2014) Current concepts on the molecular pathology of non-small cell lung carcinoma. Semin Diagn Pathol 31(4):306–313. doi:10.1053/j.semdp.2014.06.008. http://www.sciencedirect.com/science/article/pii/S0740257014000616. Lung Carcinoma: Beyond The WHO Classification

  10. Gavrielides MA, Kinnard LM, Myers KJ, Petrick N (2009) Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 251(1):26–37

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gonzalez RC, Woods RE (1992) Digital image processing, 2nd edn. Addison-Wesley Longman Publishing Co. Inc, Boston

    Google Scholar 

  12. Gould M, Maclean C, Kuschner W, Rydzak C, Owens D (2001) Accuracy of positron emission tomography for diagnosis of pulmonary nodules and mass lesions: a meta-analysis. JAMA 285(7):914–924. doi:10.1001/jama.285.7.914

    CAS  Article  PubMed  Google Scholar 

  13. Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner society: glossary of terms for thoracic imaging. Radiology 246(3):697–722. doi:10.1148/radiol.2462070712

    Article  PubMed  Google Scholar 

  14. Jing Z, Bin L, Lianfang T (2010) Lung nodule classification combining rule-based and svm. In: IEEE fifth international conference on bio-inspired computing: theories and applications, pp. 1033–1036. doi:10.1109/BICTA.2010.5645114

  15. Ko JP, Rusinek H, Jacobs EL, Babb JS, Betke M, McGuinness G, Naidich DP (2003) Small pulmonary nodules: volume measurement at chest CT—phantom study. Radiology 228(3):864–870. doi:10.1148/radiol.2283020059

    Article  PubMed  Google Scholar 

  16. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, Peitgen HO (2006) Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 25(4):417–434. doi:10.1109/TMI.2006.871547

    Article  PubMed  Google Scholar 

  17. Lee S, Kouzani A, Hu E (2010) Random forest based lung nodule classification aided by clustering. Computer Med Imaging Graph 34(7):535–542. doi:10.1016/j.compmedimag.2010.03.006. http://www.sciencedirect.com/science/article/pii/S0895611110000418

  18. Leef J, Klein J (2002) The solitary pulmonary nodule. Radiol Clin N Am 40(1):123–143, ix. doi:10.1056/NEJMcp012290

  19. Liu Y, Yang J, Zhao D, Liu J (2009) Computer aided detection of lung nodules based on voxel analysis utilizing support vector machines. In: BioMedical information engineering, 2009. FBIE 2009. International conference on future, pp 90–93. doi:10.1109/FBIE.2009.5405784

  20. Netto SMB, Silva AC, Nunes RA, Gattass M (2012) Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput Biol Med 42(11):1110–1121. doi:10.1016/j.compbiomed.2012.09.003

    Article  Google Scholar 

  21. Orozco H, Osiris Vergara Villegas O, Maynez L, Sanchez V, de Jesus Ochoa Dominguez H (2012) Lung nodule classification in frequency domain using support vector machines. In: 2012 11th international conference on information science, signal processing and their applications (ISSPA), pp. 870–875. doi:10.1109/ISSPA.2012.6310676

  22. Patil SS, Godoy MC, Sorensen JI, Marom EM (2014) Lung cancer imaging. Semin Diagn Pathol 31(4):293–305. doi:10.1053/j.semdp.2014.06.007. http://www.sciencedirect.com/science/article/pii/S0740257014000604. Lung Carcinoma: Beyond The WHO Classification

  23. Reeves A, Chan A, Yankelevitz D, Henschke C, Kressler B, Kostis W (2006) On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 25(4):435–450. doi:10.1109/TMI.2006.871548

    Article  PubMed  Google Scholar 

  24. Rivollier S, Debayle J, Pinoli JC (2010) Shape diagrams for 2d compact sets-part I: analytic convex sets. Aust J Math Anal Appl 7(2–3):1–27

    Google Scholar 

  25. Rivollier S, Debayle J, Pinoli JC (2010) Shape diagrams for 2d compact sets-part II: analytic simply connected sets. Aust J Math Anal Appl 7(2–4):1–21

    Google Scholar 

  26. Schölkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, MA, USA. ISBN: 0262194759

  27. Sivakumar S, Chandrasekar C (2013) Lung nodule detection using fuzzy clustering and support vector machines. Int J Eng Technol (IJET) 5(11):179–185

    CAS  Google Scholar 

  28. Soliman AA, Abd Ellah AH, Abou-Elheggag NA, Modhesh AA (2012) Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data. J Appl Stat 39(12):2741–2758. http://EconPapers.repec.org/RePEc:taf:japsta:v:39:y:2012:i:12:p:2741-2758

  29. Sone S, Takashima S, Li F, Yang Z, Honda T, Maruyama Y, Hasegawa M, Yamanda T, Kubo K, Hanamura K, Asakura K (1998) Mass screening for lung cancer with mobile spiral computed tomography scanner. Lancet 351(9111):1242–1245. doi:10.1016/S0140-6736(97)08229-9. http://www.sciencedirect.com/science/article/pii/S0140673697082299

  30. Tartar A, Kilic N, Akan A (2013) Classification of pulmonary nodules by using hybrid features. Comp Math Methods Med 2013. http://dblp.uni-trier.de/db/journals/cmmm/cmmm2013.htmlTartarKA13

  31. Udupa J, Herman G (1999) 3D Imaging in Medicine, Second Edition. Taylor & Francis. https://books.google.com.br/books?id=aR6PHYluq4oC

  32. Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G (2009) Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Transactions on Biomedical Engineering 56(7):1810–1820. doi:10.1109/TBME.2009.2017027

    Article  PubMed  Google Scholar 

  33. Zhang F, Song Y, Cai W, Zhou Y, Shan S, Feng D (2013) Context curves for classification of lung nodule images. In: 2013 international conference on Digital image computing: techniques and applications (DICTA), pp. 1–7. doi:10.1109/DICTA.2013.6691494

  34. Zinovev D, Feigenbaum J, Furst J, Raicu D (2011) Probabilistic lung nodule classification with belief decision trees. In: Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE, pp. 4493–4498. doi:10.1109/IEMBS.2011.6091114

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Acknowledgments

The authors acknowledge the Coordination for the Improvement of Higher Education Personnel (CAPES), the National Council for Scientific and Technological Development (CNPq), and the Foundation for the Protection of Research and Scientific and Technological Development of the State of Maranhão (FAPEMA) for their financial support.

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Affiliations

  1. Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil

    Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva & Anselmo Cardoso de Paiva

  2. State University of Rio de Janeiro, Sao Francisco de Xavier, 524, Maracana, Rio de Janeiro, RJ, 20550-900, Brazil

    Rodolfo Acatauassú Nunes

  3. Department of Computer Science, Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil

    Marcelo Gattass

Authors
  1. Antonio Oseas de Carvalho Filho
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  2. Aristófanes Corrêa Silva
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  3. Anselmo Cardoso de Paiva
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  4. Rodolfo Acatauassú Nunes
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  5. Marcelo Gattass
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Correspondence to Antonio Oseas de Carvalho Filho.

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Filho, A.O.d.C., Silva, A.C., de Paiva, A.C. et al. 3D shape analysis to reduce false positives for lung nodule detection systems. Med Biol Eng Comput 55, 1199–1213 (2017). https://doi.org/10.1007/s11517-016-1582-x

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  • DOI: https://doi.org/10.1007/s11517-016-1582-x

Keywords

  • Lung cancer
  • Medical image
  • Shape diagrams
  • Cylinder-based analysis