Emergent Segmentation of Topological Active Nets by Means of Evolutionary Obtained Artificial Neural Networks

Cristina V. Sierra, Jorge Novo, José Santos, Manuel G. Penedo

Abstract

We developed a novel segmentation method using deformable models. As deformable model we used Topological Active Nets, model which integrates features of region-based and boundary-based segmentation techniques. The deformation through time is defined by an Artificial Neural Network (ANN) that learns to move each node of the segmentation model based on its energy surrounding. The ANN is applied to each of the nodes and in different temporal steps until the final segmentation is obtained. The ANN training is obtained by simulated evolution, using differential evolution to automatically obtain the ANN that provides the emergent segmentation. The new proposal was tested in different artificial and real images, showing the capabilities of the methodology.

References

  1. Ansia, F., Penedo, M., Marin˜o, C., and Mosquera, A. (1999). A new approach to active nets. Pattern Recognition and Image Analysis, 2:76-77.
  2. Feoktistov, V. (2006). Differential Evolution: In Search of Solutions. Springer, New York, USA.
  3. Ibán˜ez, O., Barreira, N., Santos, J., and Penedo, M. (2009). Genetic approaches for topological active nets optimization. Pattern Recognition, 42:907-917.
  4. Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(2):321-323.
  5. McInerney, T., Hamarneh, G., Shenton, M., and Terzopoulos, D. (2002). Deformable organisms for automatic medical image analysis. Medical Image Analysis, 6:251-266.
  6. Novo, J., Penedo, M. G., and Santos, J. (2009). Localisation of the optic disc by means of GA-Optimised Topological Active Nets. Image and Vision Computing, 27:1572-1584.
  7. Novo, J., Santos, J., and Penedo, M. G. (2011). Optimization of Topological Active Nets with differential evolution. Lecture Notes in Computer Science: Adaptive and Natural Computing Algorithms, 6593:350-360.
  8. Price, K. and Storn, R. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341-359.
  9. Price, K., Storn, R., and Lampinen, J. (2005). Differential Evolution. A Practical Approach to Global Optimization. Springer - Natural Computing Series.
  10. Tsumiyama, K. and Yamamoto, K. (1989). Active net: Active net model for region extraction. IPSJ SIG notes, 89(96):1-8.
  11. Williams, D. J. and Shah, M. (1992). A Fast algorithm for active contours and curvature estimation. CVGIP: Image Understanding, 55(1):14-26.
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Paper Citation


in Harvard Style

V. Sierra C., Novo J., Santos J. and G. Penedo M. (2013). Emergent Segmentation of Topological Active Nets by Means of Evolutionary Obtained Artificial Neural Networks . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 44-50. DOI: 10.5220/0004195700440050


in Bibtex Style

@conference{icaart13,
author={Cristina V. Sierra and Jorge Novo and José Santos and Manuel G. Penedo},
title={Emergent Segmentation of Topological Active Nets by Means of Evolutionary Obtained Artificial Neural Networks},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={44-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004195700440050},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Emergent Segmentation of Topological Active Nets by Means of Evolutionary Obtained Artificial Neural Networks
SN - 978-989-8565-39-6
AU - V. Sierra C.
AU - Novo J.
AU - Santos J.
AU - G. Penedo M.
PY - 2013
SP - 44
EP - 50
DO - 10.5220/0004195700440050