LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

David Feltell, Li Bai

2008

Abstract

This paper presents a novel 3D brain segmentation method based on level sets and bio-inspired methodologies. Level set segmentation methods, although highly promising, require manual selection of seed positions and thereshold parameters, along with manual reinitialisation to a new level set surface for each candidate region. Here, the use of swarm intelligent mechanisms is used to provide all the statistical data and sample points required, allowing automatic initialisation of multiple level set solvers. This is shown by segmentation of white matter, grey matter and cerebro-spinal fluid in a simulated T1 MRI scan, followed by direct comparison between a commercial level application - FMRIB’s FAST - and the ground truth anatomical model.

References

  1. Bocchi, L., Ballerini, L., and Hssler, S. (2005). A new evolutionary algorithm for image segmentation. Applications on Evolutionary Computing, pages 264-273.
  2. Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press.
  3. Bourjot, C., Chevrier, V., and Thomas, V. (2003). A new swarm mechanism based on social spiders colonies: From web weaving to region detection. Web Intelligence and Agent System, 1(1):13-32.
  4. Bruinsma, O. (1979). An Analysis of Building Behaviour of the Termite Macrotermes Subhyalinus (Rambur). PhD thesis, Landbouwhogeschool, Wageningen.
  5. Camazine, S., Deneubourg, J.-L., Franks, N., Sneyd, J., Theraulaz, G., and Bonabeau, E. (2001). SelfOrganization in Biological Systems. Princeton University Press.
  6. D. Adalsteinsson, J. S. (1995). A fast level set method for propagating interfaces. J. Comp. Phys., 118:269-277.
  7. Feltell, D. and Bai, L. (2004). Swarm robotics for construction. In 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK.
  8. Liu, J., Tang, Y., and Cao, Y. (1997). An evolutionary autonomous agents approach to image feature extraction. IEEE Transactions on Evolutionary Computation, 1:141-158.
  9. McConnell BIC (2007). Brainweb: Simulated brain database, Montreal Neurological Institute. http://www.bic.mni.mcgill.ca/brainweb/.
  10. Monmarche, N. (1999). On data clustering with artificial ants. In Freitas, A. A., editor, Data Mining with Evolutionary Algorithms: Research Directions, pages 23- 26, Orlando, Florida. AAAI Press.
  11. Monmarche, N., Slimane, M., and Venturini, G. (1999). On improving clustering in numerical databases with artificial ants. In ECAL 7899: Proceedings of the 5th European Conference on Advances in Artificial Life, pages 626-635, London, UK. Springer-Verlag.
  12. Phillips, C. L. (1999). The level set method. The MIT Undergraduate Journal of Mathematics, 1:155-164.
  13. Ramos, V. and Almeida, F. (2000). Artificial ant colonies in digital image habitats - a mass behaviour effect study on pattern recognition. In 2nd Int. Workshop on Ant Algorithms (ANTS 2000), pages 113-116, Brussels, Belgium.
  14. Schockaert, S., Cock, M. D., Cornelis, C., and Kerre, E. E. (2004). Fuzzy ant based clustering. In International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 2004), pages 342-349, Brussels, Belguim.
  15. Sethian, J. (1996). Level set methods: An act of violence - evolving interfaces in geometry, fluid mechanics, computer vision and materials sciences. American Scientist.
  16. Sethian, J. (1999). Level Set Methods and Fast Marching Methods. Cambridge University Press.
  17. Smith, S. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155.
  18. Vizine, A., de Castro, L., Hruschka, E., and Gudwin, R. (2005). Towards improving clustering ants: An adaptive ant clustering algorithm. Informatica, 29(2):143- 154.
  19. Whitaker, R. (1998). A level-set approach to 3d reconstruction from range data. International Journal of Computer Vision, 29(3):203-231.
  20. Zhang, Y., Brady, M., and Smith, S. (2001). Segmentation of brain mr images through a hidden markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging, 20(1):45-57.
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Paper Citation


in Harvard Style

Feltell D. and Bai L. (2008). LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 210-217. DOI: 10.5220/0001067702100217


in Bibtex Style

@conference{biosignals08,
author={David Feltell and Li Bai},
title={LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={210-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001067702100217},
isbn={978-989-8111-18-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)
TI - LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization
SN - 978-989-8111-18-0
AU - Feltell D.
AU - Bai L.
PY - 2008
SP - 210
EP - 217
DO - 10.5220/0001067702100217