Using Evolved Artificial Neural Networks for Providing an Emergent Segmentation with an Active Net Model

  • Jorge Novo
  • Cristina V. Sierra
  • José Santos
  • Manuel G. Penedo
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 234)


A novel segmentation method using deformable models for medical image segmentation was developed. As deformable model, Topological Active Nets (TAN) were used, model which integrates features of region-based and boundary-based segmentation techniques. The model deformation through time is controlled by an Artificial Neural Network (ANN) that learns how to move the nodes of the model based on their energy surrounding. The ANN is applied to each of the nodes and in different temporal steps until the final segmentation is reached. The ANN training is obtained by simulated evolution, using Differential Evolution (DE) to automatically obtain the ANN that provides the emergent segmentation. The proposed methodology was adapted and tested in three different medical domains, that is, Computed Tomography (CT), Cone Beam Computed Tomography (CBCT) and eye fundus images to demonstrate the potential of the segmentation technique.


Topological Active Nets Differential Evolution Artificial Neural Networks Medical Imaging 



This paper has been partly funded by the Ministry of Science and Innovation through grant contracts TIN2011-25476 and TIN2011-27294 and by the Consellería de Industria, Xunta de Galicia, through grant contract 10/CSA918054PR.


  1. 1.
    Tsumiyama, K., Yamamoto, K.: Active net: active net model for region extraction. IPSJ SIG Notes 89(96), 1–8 (1989)Google Scholar
  2. 2.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(2), 321–323 (1988)CrossRefGoogle Scholar
  3. 3.
    Ansia, F., Penedo, M., Mariño, C., Mosquera, A.: A new approach to active nets. Pattern Recognit. Image Anal. 2, 76–77 (1999)Google Scholar
  4. 4.
    Williams, D., Shah, M.: A fast algorithm for active contours and curvature estimation. CVGIP Image Underst. 55(1), 14–26 (1992)Google Scholar
  5. 5.
    Ibáñez, O., Barreira, N., Santos, J., Penedo, M.: Genetic approaches for topological active nets optimization. Pattern Recogn. 42, 907–917 (2009)CrossRefMATHGoogle Scholar
  6. 6.
    Novo, J., Penedo, M.G., Santos, J.: Localisation of the optic disc by means of GA-optimised topological active nets. Image Vis. Comput. 27, 1572–1584 (2009)CrossRefGoogle Scholar
  7. 7.
    Novo, J., Barreira, N., Penedo, M., Santos, J.: Topological active volume 3D segmentation model optimized with genetic approaches. Nat. Comput. 11, 161–174 (2012)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Novo, J., Santos, J., Penedo, M.G.: Optimization of topological active nets with differential evolution. Lect. Notes Comput. Sci. Adapt. Nat. Comput. Algorithms 6593, 350–360 (2011)CrossRefGoogle Scholar
  9. 9.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. In: EUROGEN: Evolutionary Methods for Design. Optimisation, and Control. 2002, 95–100 (2001)Google Scholar
  10. 10.
    Novo, J., Penedo, M., Santos, J.: Evolutionary multiobjective optimization of topological active nets. Pattern Recogn. Lett. 31, 1781–1794 (2010)CrossRefGoogle Scholar
  11. 11.
    Novo, J., Penedo, M.G., Santos, J.: Multiobjective optimization of the 3D topological active volume segmentation model. In: ICAART: International Conference on Agents and. Artificial Intelligence. 2011, 236–241 (2011)Google Scholar
  12. 12.
    McInerney, T., Hamarneh, G., Shenton, M., Terzopoulos, D.: Deformable organisms for automatic medical image analysis. Med. Image Anal. 6, 251–266 (2002)CrossRefGoogle Scholar
  13. 13.
    Price, K., Storn, R.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Price, K., Storn, R., Lampinen, J.: Differential evolution. A Practical Approach to Global Optimization (Natural Computing Series). Springer, New York (2005)Google Scholar
  15. 15.
    Feoktistov, V.: Differential Evolution: In Search of Solutions. Springer, New York (2006)Google Scholar
  16. 16.
    Scarfe, W., Farman, A.: What is cone beam CT and how does it work? Dent. Clin. North Am. 52, 707–730 (2008)CrossRefGoogle Scholar
  17. 17.
    Greef, S.D., Willems, G.: Three-dimensional cranio-facial reconstruction in forensic identification: latest progress and new tendencies in the 21st century. J. Forensic Sci. 50(1), 12–17 (2005)CrossRefGoogle Scholar
  18. 18.
    VARIA: VARPA Retinal Images for Authentication. Varpa Web site at. (2008)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jorge Novo
    • 1
  • Cristina V. Sierra
    • 1
  • José Santos
    • 1
  • Manuel G. Penedo
    • 1
  1. 1.Computer Science DepartmentUniversity of A CoruñaA CoruñaSpain

Personalised recommendations