Central Medialness Adaptive Strategy for 3D Lung Nodule Segmentation in Thoracic CT Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)

Abstract

In this paper, a Hessian-based strategy, based on the central medialness adaptive principle, was adapted and proposed in a multiscale approach for the 3D segmentation of pulmonary nodules in chest CT scans. This proposal is compared with another well stated Hessian based strategy of the literature, for nodule extraction, in order to demonstrate its accuracy.

Several scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were employed in the test and validation procedure. The scans include a large and heterogeneous set of 569 solid and mostly solid nodules with a large variability in the nodule characteristics and image conditions. The results demonstrated that the proposal offers correct results, similar to the performance of the radiologists, providing accurate nodule segmentations that perform the desirable scenario for a posterior analysis and the eventual lung cancer diagnosis.

Keywords

Computer-aided diagnosis Thoracic CT imaging Lung cancer Pulmonary nodule segmentation Hessian-based approaches 

References

  1. 1.
    Greenlee, R.T., Murray, T., Bolden, S., Wingo, P.A.: Cancer statistics. CA Cancer J. Clin. 2000(50), 7–33 (2000)CrossRefGoogle Scholar
  2. 2.
    Armato, S.G., Giger, M.L., MacMahon, H.: Automated detection of lung nodules in ct scans: preliminary results. Med. Phys. 28(8), 1552–1561 (2001)CrossRefGoogle Scholar
  3. 3.
    Setio, A.A.A., Jacobs, C., Gelderblom, J., van Ginneken, B.: Automatic detection of large pulmonary solid nodules in thoracic CT images. Med. Phys. 42(10), 5642–5653 (2015)CrossRefGoogle Scholar
  4. 4.
    Gu, Y., Kumar, V., Hall, L.O., Goldgof, D.B., et al.: Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recogn. 46(3), 692–702 (2013)CrossRefGoogle Scholar
  5. 5.
    Zhang, X., Zhang, C., Tang, W., Wei, Z.: Medical image segmentation using improved fcm. Sci. China Inf. Sci. 55(5), 1052–1061 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Qiang, Y., Zhang, X., Ji, G., Zhao, J.: Measuring agreement in method comparison studies. J. Comput. Theoret. Nanosci. 12, 1972–1976 (2015)CrossRefGoogle Scholar
  7. 7.
    Chen, B., Kitasaka, T., Honma, H., Takabatake, H., et al.: Automatic segmentation of pulmonary blood vessels and nodules basedon local intensity structure analysis and surface propagation in 3D chest CT images. Int. J. Comput. Assist. Radiol. Surg. 2010, 465–482 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhao, J., Ji, G., Xia, Y., Zhang, X.: Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation. Int. J. Bio-Inspired Comput. 7(1), 62–67 (2015)CrossRefGoogle Scholar
  9. 9.
    Murphy, K., van Ginneken, B., Schilham, A.M.R., et al.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 13, 757–770 (2009)CrossRefGoogle Scholar
  10. 10.
    Novo, J., Gonçalves, L., Mendonça, A.M., Campilho, A.: 3D lung nodule candidates detection in multiple scales. In: MVA 2015-IAPR International Conference on Machine Vision Applications, pp. 61–64 (2015)Google Scholar
  11. 11.
    Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model-based detection of tubular structures in 3d images. Comput. Vis. Image Underst. 80(2), 130–171 (2000)CrossRefMATHGoogle Scholar
  12. 12.
    Rudyanto, R.D., Kerkstra, S., van Rikxoort, E.M., et al.: Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med. Image Anal. 18(7), 1217–1232 (2014)CrossRefGoogle Scholar
  13. 13.
    Armato, S.G., McLennan, G., Bidaut, L., 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, 915–931 (2011)CrossRefGoogle Scholar
  14. 14.
    Bland, J.M., Altman, D.G.: Measuring agreement in method comparison studies. Stat. Methods Med. Res. 8(2), 135–160 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Luis Gonçalves
    • 1
    • 2
  • Jorge Novo
    • 3
  • Aurélio Campilho
    • 1
    • 2
  1. 1.INESC TEC - INESC Technology and SciencePortoPortugal
  2. 2.Faculdade de EngenhariaUniversidade Do PortoPortoPortugal
  3. 3.Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain

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