Skip to main content
SpringerLink
Log in

Multistage segmentation model and SVM-ensemble for precise lung nodule detection

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Lung cancer detection at its initial stages increases the survival chances of patients. Automatic detection of lung nodules facilitates radiologists during the diagnosis. However, there is a challenge of false positives in automated systems which may lead to wrong findings. Precise segmentation facilitates to accurately extract nodules from lung CT images in order to improve performance of the diagnostic method.

Methods

A multistage segmentation model is presented in this study. The lung region is extracted by applying corner-seeded region growing combined with differential evolution-based optimal thresholding. In addition to this, morphological operations are applied in boundary smoothing, hole filling and juxtavascular nodule extraction. Geometric properties along with 3D edge information are applied to extract nodule candidates. Geometric texture features descriptor (GTFD) followed by support vector machine-based ensemble classification is employed to distinguish actual nodules from the candidate set.

Results

A publicly available dataset, namely lung image database consortium and image database resource initiative, is used to evaluate performance of the proposed method. The classification is performed over GTFD feature vector and the results show 99% accuracy, 98.6% sensitivity and 98.2% specificity with 3.4 false positives per scan (FPs/scan).

Conclusion

A lung nodule detection method is presented to facilitate radiologists in accurately diagnosing cancer from CT images. Results indicate that the proposed method has not only reduced FPs/scan but also significantly improved sensitivity as compared to related studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Organization WH (2017) Cancer fact sheet. http://www.who.int/mediacentre/factsheets/fs297/en/. Accessed 09 June 2017

  2. Gould MK, Fletcher J, Iannettoni MD, Lynch WR, Midthun DE, Naidich DP, Ost DE (2007) Evaluation of patients with pulmonary nodules: when is it lung cancer? ACCP evidence-based clinical practice guidelines. Chest J 132(3–suppl):108S–130S

    Article  Google Scholar 

  3. Petkovska I, Brown MS, Goldin JG, Kim HJ, McNitt-Gray MF, Abtin FG, Ghurabi RJ, Aberle DR (2007) The effect of lung volume on nodule size on CT. Acad Radiol 14(4):476–485

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bach PB, Mirkin JN, Oliver TK, Azzoli CG, Berry DA, Brawley OW, Byers T, Colditz GA, Gould MK, Jett JR (2012) Benefits and harms of CT screening for lung cancer: a systematic review. JAMA 307(22):2418–2429

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Endo M, Aramaki T, Asakura K, Moriguchi M, Akimaru M, Osawa A, Hisanaga R, Moriya Y, Shimura K, Furukawa H (2012) Content-based image-retrieval system in chest computed tomography for a solitary pulmonary nodule: method and preliminary experiments. Int J Comput Assist Radiol Surg 7(2):331–338

    Article  PubMed  Google Scholar 

  6. Yim Y, Hong H (2008) Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images. Comput Biol Med 38(8):845–857

    Article  PubMed  Google Scholar 

  7. Naqi SM, Sharif M (2017) Recent developments in computer aided diagnosis for lung nodule detection from CT images: a review. Curr Med Imaging Rev 13(1):3–19

    Article  Google Scholar 

  8. van Ginneken B, Armato SG III, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14(6):707–722

    Article  PubMed  Google Scholar 

  9. Tan M, Deklerck R, Jansen B, Bister M, Cornelis J (2011) A novel computer-aided lung nodule detection system for CT images. Med Phys 38(10):5630–5645

    Article  PubMed  Google Scholar 

  10. Dehmeshki J, Ye X, Lin X, Valdivieso M, Amin H (2007) Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput Med Imaging Graph 31(6):408–417

    Article  PubMed  Google Scholar 

  11. Ozekes S, Osman O, Ucan ON (2008) Nodule detection in a lung region that’s segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding. Korean J Radiol 9(1):1–9

    Article  PubMed  PubMed Central  Google Scholar 

  12. Choi W-J, Choi T-S (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Programs Biomed 113(1):37–54

    Article  PubMed  Google Scholar 

  13. Taşcı E, Uğur A (2015) Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs. J Med Syst 39(5):46

    Article  PubMed  Google Scholar 

  14. Riccardi A, Petkov TS, Ferri G, Masotti M, Campanini R (2011) Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med Phys 38(4):1962–1971

    Article  PubMed  Google Scholar 

  15. Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G (2009) Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans Biomed Eng 56(7):1810–1820

    Article  PubMed  Google Scholar 

  16. Mukhopadhyay S (2016) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 29(1):86–103

    Article  PubMed  Google Scholar 

  17. Armato SG III, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys 28(8):1552–1561

    Article  PubMed  Google Scholar 

  18. Elizabeth DS, Nehemiah HK, Raj C, Kannan A (2012) A novel segmentation approach for improving diagnostic accuracy of CAD systems for detecting lung cancer from chest computed tomography images. J Data Inf Qual 3(2):4

    Google Scholar 

  19. Han H, Li L, Han F, Song B, Moore W, Liang Z (2015) Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE J Biomed Health Inform 19(2):648–659

    Article  PubMed  Google Scholar 

  20. Tan Y, Schwartz LH, Zhao B (2013) Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. Med Phys 40(4):4350201–4350210

    Article  Google Scholar 

  21. Shen S, Bui AA, Cong J, Hsu W (2015) An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 57:139–149

    Article  PubMed  Google Scholar 

  22. Cascio D, Magro R, Fauci F, Iacomi M, Raso G (2012) Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med 42(11):1098–1109

    Article  PubMed  CAS  Google Scholar 

  23. Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113(1):202–209

    Article  PubMed  Google Scholar 

  24. Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 12(10):1799–1808

    Article  PubMed  Google Scholar 

  25. de Carvalho Filho AO, de Sampaio WB, Silva AC, de Paiva AC, Nunes RA, Gattass M (2014) Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif Intell Med 60(3):165–177

    Article  PubMed  Google Scholar 

  26. Teramoto A, Fujita H (2013) Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J Comput Assist Radiol Surg 8(2):193–205

    Article  PubMed  Google Scholar 

  27. Cuevas E, Zaldivar D, Pérez-Cisneros M (2010) A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst Appl 37(7):5265–5271

    Article  Google Scholar 

  28. Dhara AK, Mukhopadhyay S, Mehre SA, Khandelwal N, Prabhakar N, Garg M, Kalra N (2017) A study of retrieval accuracy of pulmonary nodules based on external attachment. In: SPIE medical imaging. International Society for Optics and Photonics, pp 101343T–101346

  29. Lafarge F, Descombes X (2010) Geometric feature extraction by a multimarked point process. IEEE Trans Pattern Anal Mach Intell 32(9):1597–1609

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  31. Yang F, Xu Y-Y, Wang S-T, Shen H-B (2014) Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing 131:113–123

    Article  Google Scholar 

  32. Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA (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

    Article  PubMed  PubMed Central  Google Scholar 

  33. Moyer VA (2014) Screening for lung cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 160(5):330–338

    Article  PubMed  Google Scholar 

  34. Jacobs C, Rikxoort EM, Murphy K, Prokop M, Schaefer-Prokop CM, Ginneken B (2016) Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database. Eur Radiol 26(7):2139–2147

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Wah Cantt, Pakistan

    Syed Muhammad Naqi, Muhammad Sharif & Mussarat Yasmin

  2. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan

    Syed Muhammad Naqi

Authors
  1. Syed Muhammad Naqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Sharif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mussarat Yasmin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Muhammad Naqi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies performed with human participants or animals by any of the authors.

Informed consent

This article does not contain patient data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naqi, S.M., Sharif, M. & Yasmin, M. Multistage segmentation model and SVM-ensemble for precise lung nodule detection. Int J CARS 13, 1083–1095 (2018). https://doi.org/10.1007/s11548-018-1715-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-018-1715-9

Keywords

Search

Navigation