Advertisement

Segmentation of Two-Phase Flow: A Free Representation for Levet Set Method with a Priori Knowledge

  • Mauren Louise Sguario
  • Lucia Valeria Ramos de Arruda
  • Iuri Nack Buss
  • Henderson Cari Nascimento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8641)

Abstract

In this paper, the segmentation approaches based on active contours-based model were united. The result is a new approach which improves the average freely previously trained format, using the method of contour active for Level Set. In this case, there is no restriction evolution of the interface as other approaches that use the junction active contours and prior knowledge. This approach was chosen for the correct identification of the form of gas bubbles in gas-liquid two-phase flow. The main objective of this work is to provide a system of validation for the various approaches of flow instrumentation widely used. The promising results indicate that the system of image segmentation by the proposed approach gives good results and can be used as an efficient method of validation to other existing approaches.

Keywords

Priori Shape Level Set Method Two-phase flow 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shi, L., Zhou, Z., Ren, R.: Parameter measurements of two-phase bubbly flow using digital image processing, pp. 3858–3861 (2004)Google Scholar
  2. 2.
    Tri, B.S.K., Dinh, B., Choi, T.-S.: Application of image processing techniques to air/water two-phase flow. In: Proc. SPIE 3808, pp. 725–730 (1999)Google Scholar
  3. 3.
    Galindo, E., Larralde-Corona, C.P., Brito, T.: Development of advanced image analysis techniques for the in situ characterization of multiphase dispersions occurring in bioreactors. Journal of Biotechnology 116(3), 261–270 (2005)CrossRefGoogle Scholar
  4. 4.
    Hanafizadeh, P., Ghanbarzadeh, S., Saidi, M.H.: Visual technique for detection of gas liquid two phase flow regime in the airlift pump. Journal of Petroleum Science and Engineering 75(3-4), 327–335 (2011)CrossRefGoogle Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  6. 6.
    Chunming Li, C.G., Xu, C., Fox, M.: Level set evolution without re-initialization: A new variational formulation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 430–436 (2005)Google Scholar
  7. 7.
    Wallis, G.B.: One-dimensional two-phase flow. McGraw-Hill (1969)Google Scholar
  8. 8.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1987)CrossRefGoogle Scholar
  9. 9.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79(1), 12–49 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Ma, Z., da Silva Tavares, J.M.R., Jorge, R.M.N.J.: A review on the current segmentation algorithms for medical images. In: IMAGAPP 2009 (2009)Google Scholar
  11. 11.
    Sethian, A.J.: Level set methods: An act of violence. American Scientist (1996)Google Scholar
  12. 12.
    Cootes, T., Baldock, E., Graham, J.: An introduction to active shape models. Image Processing and Analysis, 223–248 (2000)Google Scholar
  13. 13.
    Tomoshige, S., Oost, E., Shimizu, A., Watanabe, H., Nawano, S.: A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast {CT} images. Medical Image Analysis 18(1), 130–143 (2014), http://www.sciencedirect.com/science/article/pii/S1361841513001473 CrossRefGoogle Scholar
  14. 14.
    Terzopoulos, D., Witkin, A., Kass, M.: Constraints on deformable models: Recovering 3d shape and nongrid motion. Artif. Intell. 36(1), 91–123 (1988), http://dx.doi.org/10.1016/0004-37028890080-X CrossRefzbMATHGoogle Scholar
  15. 15.
    Delingette, H., Epidaure, P.: Simplex meshes: a general representation for 3d shape reconstruction. Tech. Rep. (1994)Google Scholar
  16. 16.
    Mcinerney, T., Terzopoulos, D.: Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Transactions on Medical Imaging 18, 840–850 (1999)CrossRefGoogle Scholar
  17. 17.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996), http://www.sciencedirect.com/science/article/pii/S1361841596800077 CrossRefGoogle Scholar
  18. 18.
    Jain, A.K., Zhong, Y., Dubuisson-Jolly, M.-P.: Deformable template models: A review. Signal Processing 71(2), 109–129 (1998), http://www.sciencedirect.com/science/article/pii/S016516849800139X CrossRefzbMATHGoogle Scholar
  19. 19.
    Montagnat, J., Delingette, H., Ayache, N.: A review of deformable surfaces: Topology, geometry and deformation. Image and Vision Computing 19, 1023–1040 (2001)CrossRefGoogle Scholar
  20. 20.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995), http://dx.doi.org/10.1109/34.368173, doi:10.1109/34.368173CrossRefGoogle Scholar
  21. 21.
    Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W.E., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imag., 137–154 (2003)Google Scholar
  22. 22.
    Rousson, M., Paragios, N.: Shape priors for level set representations (2004)Google Scholar
  23. 23.
    Diop, E.H.S., Burdin, V.: Bi-planar image segmentation based on variational geometrical active contours with shape priors. Medical Image Analysis 17(2), 165–181 (2013), http://www.sciencedirect.com/science/article/pii/S1361841512001351 CrossRefGoogle Scholar
  24. 24.
    Leventon, M.E., Grimson, Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 316–323 (2000), http://dx.doi.org/10.1109/cvpr.2000.855835
  25. 25.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge UPress (1999)Google Scholar
  26. 26.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (June 2007)Google Scholar
  27. 27.
    Dorini, L.B.: Transformação de imagens baseadas em morfologia matemática, Ph.D. dissertation, Unicamp - Universidade Estadual de Campinas, Campinas (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mauren Louise Sguario
    • 1
  • Lucia Valeria Ramos de Arruda
    • 1
  • Iuri Nack Buss
    • 1
  • Henderson Cari Nascimento
    • 1
  1. 1.Federal Technological University of ParanaCuritibaBrazil

Personalised recommendations