Signal, Image and Video Processing

, Volume 13, Issue 1, pp 53–60 | Cite as

Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme

  • Ratishchandra HuidromEmail author
  • Yambem Jina Chanu
  • Khumanthem Manglem Singh
Original Paper


Pulmonary nodule detection is an image processing approach to detect lung cancer from thoracic computed tomography (CT) images. Lung segmentation is the first step to segment lung regions from CT images. Nodule candidates are detected from the segmented lung regions. Further, nodule classification is performed to identify the true nodules from the false positives. In a recent paper, linear discriminant analysis (LDA) shows better performance than quadratic discriminant analysis for nodule classification. In the proposed method, the performance of the existing LDA method is improved by introducing additional discriminant features extracted by using multiple discriminant analysis. Further, a noble nonlinear classifier is used to overcome the limitation of the linear classifier. For the nonlinear classification, multilayer perceptron is used using a new powerful learning algorithm. The new learning algorithm is a combination of genetic algorithm (GA) and particle swarm optimization. Finally, the performance of the proposed method is compared with the existing LDA and convolutional neural network methods. The new learning algorithm of the proposed method is also compared with backpropagation (BP) and GA optimized BP algorithms. The overall performance of the proposed method is remarkable.


Nodule detection Computer aided detection Lung cancer Multiple discriminant features Genetic algorithm Particle swarm optimization 

Supplementary material

11760_2018_1327_MOESM1_ESM.txt (1 kb)
Supplementary material 1 (txt 0 KB)
11760_2018_1327_MOESM2_ESM.xlsx (14 kb)
Supplementary material 2 (xlsx 13 KB)
11760_2018_1327_MOESM3_ESM.xlsx (14 kb)
Supplementary material 3 (xlsx 13 KB)
11760_2018_1327_MOESM4_ESM.docx (35 kb)
Supplementary material 4 (docx 35 KB)
11760_2018_1327_MOESM5_ESM.txt (6 kb)
Supplementary material 5 (txt 6 KB)


  1. 1.
    El-Baz, A., Beache, G.M., Gimel’farb, G., Suzuki, K., Okada, K., Elnakib, A., Soliman, A., Abdollahi, B.: Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int. J. Biomed. Imaging 2013, 1–46 (2013). Google Scholar
  2. 2.
    Brown, M.S., McNitt-Gray, M.F., Goldin, J.G., Suh, R.D., Sayre, J.W., Aberle, D.R.: Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Trans. Med. Imaging 20(12), 1242–1250 (2001)CrossRefGoogle Scholar
  3. 3.
    Gurcan, M.N., Sahiner, B., Petrick, N., Chan, H.P., Kazerooni, E.A., Cascade, P.N., Hadjiiski, L.: Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med. Phys. 29(11), 2552–2558 (2002)CrossRefGoogle Scholar
  4. 4.
    Mekada, Y., Kusanagi, T., Hayase, Y., Mori, K., Ji, Hasegawa, Ji, Toriwaki, Mori, M., Natori, H.: Detection of small nodules from 3D chest X-ray CT images based on shape features. Int. Congr. Ser. 1256, 971–976 (2003)CrossRefGoogle Scholar
  5. 5.
    Awai, K., Murao, K., Ozawa, A., Komi, M., Hayakawa, H., Hori, S., Nishimura, Y.: Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists detection performance. Radiology 230(2), 347–352 (2004)CrossRefGoogle Scholar
  6. 6.
    Mendonça, P.R., Bhotika, R., Sirohey, S.A., Turner, W.D., Miller, J.V., Avila, R.S.: Model-based analysis of local shape for lesion detection in CT scans. In: International conference on medical image computing and computer-assisted intervention, pp. 688–695. Springer, Berlin (2005)Google Scholar
  7. 7.
    Pu, J., Zheng, B., Leader, J.K., Wang, X.H., Gur, D.: An automated CT based lung nodule detection scheme using geometric analysis of signed distance field. Med. Phys. 35(8), 3453–3461 (2008)CrossRefGoogle Scholar
  8. 8.
    Ye, X., Lin, X., Dehmeshki, J., Slabaugh, G., Beddoe, G.: Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans. Biomed. Eng. 56(7), 1810–1820 (2009)CrossRefGoogle Scholar
  9. 9.
    Riccardi, A., Petkov, T.S., Ferri, G., Masotti, M., Campanini, R.: Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med. Phys. 38(4), 1962–1971 (2011)CrossRefGoogle Scholar
  10. 10.
    Tartar, A., Kılıç, N., Akan, A.: A new method for pulmonary nodule detection using decision trees. In: 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 7355–7359 (2013)Google Scholar
  11. 11.
    Han, H., Li, L., Han, F., Zhang, H., Moore, W., Liang, Z.: Vector quantization-based automatic detection of pulmonary nodules in thoracic CT images. In: IEEE nuclear science symposium and medical imaging conference (NSS/MIC), pp. 1–4 (2013)Google Scholar
  12. 12.
    Messay, T., Hardie, R.C., Rogers, S.K.: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 14(3), 390–406 (2010)CrossRefGoogle Scholar
  13. 13.
    Tan, M., Deklerck, R., Jansen, B., Bister, M., Cornelis, J.: A novel computer-aided lung nodule detection system for CT images. Med. Phys. 38(10), 5630–5645 (2011)CrossRefGoogle Scholar
  14. 14.
    Liu, X., Hou, F., Qin, H., Hao, A.: A cade system for nodule detection in thoracic CT images based on artificial neural network. Sci. China Inf. Sci. 60(7), 072106 (2017)CrossRefGoogle Scholar
  15. 15.
    Li, W., Cao, P., Zhao, D., Wang, J.: Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput. Math. Methods Med. 2016, 1–7 (2016). Google Scholar
  16. 16.
    Huang, X., Shan, J., Vaidya, V.: Lung nodule detection in CT using 3D convolutional neural networks. In: IEEE 14th international symposium on biomedical imaging (ISBI 2017), pp. 379–383 (2017)Google Scholar
  17. 17.
    Bai, J., Huang, X., Liu, S., Song, Q., Bhagalia, R.: Learning orientation invariant contextual features for nodule detection in lung CT scans. In: IEEE 12th international symposium on biomedical imaging (ISBI), pp. 1135–1138 (2015)Google Scholar
  18. 18.
    Deb, K.: An introduction to genetic algorithms. Sadhana 24(4–5), 293–315 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Shi, Y., Eberhart, RC.: Empirical study of particle swarm optimization. In: Proceedings of IEEE the congress on evolutionary computation, CEC 99, vol. 3, pp. 1945–1950 (1999)Google Scholar
  20. 20.
    Huidrom, R., Chanu, YJ., Singh, KM.: A fast automated lung segmentation method for the diagnosis of lung cancer. In: IEEE region 10 conference TENCON 2017, pp. 1499–1502. (2017)
  21. 21.
    Hardie, R.C., Rogers, S.K., Wilson, T., Rogers, A.: Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs. Med. Image Anal. 12(3), 240–258 (2008)CrossRefGoogle Scholar
  22. 22.
    Jin, X., Han, J.: K-medoids clustering. In: Encyclopedia of machine learning and data mining, Springer, Berlin. pp 1–3 (2016)Google Scholar
  23. 23.
    Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al.: 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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Ratishchandra Huidrom
    • 1
    Email author
  • Yambem Jina Chanu
    • 1
  • Khumanthem Manglem Singh
    • 1
  1. 1.National Institute of TechnologyManipurIndia

Personalised recommendations