MULTIOBJECTIVE OPTIMIZATION OF THE 3D TOPOLOGICAL ACTIVE VOLUME SEGMENTATION MODEL

Jorge Novo, Manuel G. Penedo, José Santos

Abstract

In this work it is proposed an evolutionary multiobjective methodology for the optimization of topological active volumes. This is a 3D deformable model that integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by energy functions that must be minimized. Most optimization algorithms need an experimental tuning of the energy parameters of the model in order to obtain the best adjusted segmentation. To avoid the step of the parameter tuning, we developed an evolutionary multiobjective optimization that considers the optimization of several objectives in parallel. The proposed methodology is based on the SPEA2 algorithm, adapted to our application, to obtain the Pareto optimal individuals. The proposed method was tested on several representative images from different domains yielding highly accurate results.

References

  1. Ballerini, L. (1999). Medical image segmentation using genetic snakes. In Proceedings of SPIE: Application and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, volume 3812, pages 13-23.
  2. Barreira, N. and Penedo, M. G. (2005). Topological Active Volumes. EURASIP Journal on Applied Signal Processing, 13(1):1937-1947.
  3. Bro-Nielsen, M. (1994). Active nets and cubes. Technical Report 13, IMM, Technical University of Denmark.
  4. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley, Chichester, UK.
  5. Ibán˜ez, O., Barreira, N., Santos, J., and Penedo, M. (2009). Genetic approaches for topological active nets optimization. Pattern Recognition, 42:907-917.
  6. Jaimes, A. and Coello, C. (2009). Multi-objective evolutionary algorithms: A review of the state-of-the-art and some of their applications in chemical engineering. In World Scientific, pages 61-90.
  7. Jones, T. N. and Metaxas, D. N. (1997). Automated 3D segmentation using deformable models and fuzzy affinity. 15th International Conference on Information Processing in Medical Imaging - LNCS, 1230:113-126.
  8. Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(2):321-323.
  9. McInerney, T. and Terzopoulos, D. (1999). Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Transactions on Medical Imaging, 18(10):840-850.
  10. Novo, J., Barreira, N., Santos, J., and Penedo, M. G. (2007). Topological active volumes optimization with genetic approaches. XII Conference of the Spanish Association for the Artificial Intelligence, 2:41-50.
  11. Qiu, B., Clarysse, P., Montagnat, J., Janier, M., and Vray, D. (2004). Comparison of 3D deformable models for in vivo measurements of mouse embryo from 3D ultrasound images. In Ultrasonics Symposium, 2004 IEEE, Vol. 1, pages 748-751.
  12. Séguier, R. and Cladel, N. (2003a). Genetic snakes: Application on lipreading. In International Conference on Artificial Neural Networks and Genetic Algorithms.
  13. Séguier, R. and Cladel, N. (2003b). Multiobjectives genetic snakes: Application on audio-visual speech recognition. In 4th EURASIP Conference, pages 625-630.
  14. Tsumiyama, K. and Yamamoto, K. (1989). Active net: Active net model for region extraction. IPSJ SIG notes, 89(96):1-8.
  15. Zitzler, E., Laumanns, M., and Thiele, L. (2002). SPEA2: Improving the strength pareto evolutionary algorithm. In EUROGEN 2001, Evolutionary Methods for Design, Optimisation, and Control, pages 95-100.
Download


Paper Citation


in Harvard Style

Novo J., G. Penedo M. and Santos J. (2011). MULTIOBJECTIVE OPTIMIZATION OF THE 3D TOPOLOGICAL ACTIVE VOLUME SEGMENTATION MODEL . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 236-241. DOI: 10.5220/0003144302360241


in Bibtex Style

@conference{icaart11,
author={Jorge Novo and Manuel G. Penedo and José Santos},
title={MULTIOBJECTIVE OPTIMIZATION OF THE 3D TOPOLOGICAL ACTIVE VOLUME SEGMENTATION MODEL},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={236-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003144302360241},
isbn={978-989-8425-40-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - MULTIOBJECTIVE OPTIMIZATION OF THE 3D TOPOLOGICAL ACTIVE VOLUME SEGMENTATION MODEL
SN - 978-989-8425-40-9
AU - Novo J.
AU - G. Penedo M.
AU - Santos J.
PY - 2011
SP - 236
EP - 241
DO - 10.5220/0003144302360241