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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Shi, L., Zhou, Z., Ren, R.: Parameter measurements of two-phase bubbly flow using digital image processing, pp. 3858–3861 (2004)
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)
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)
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)
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)
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)
Wallis, G.B.: One-dimensional two-phase flow. McGraw-Hill (1969)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1987)
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)
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)
Sethian, A.J.: Level set methods: An act of violence. American Scientist (1996)
Cootes, T., Baldock, E., Graham, J.: An introduction to active shape models. Image Processing and Analysis, 223–248 (2000)
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
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
Delingette, H., Epidaure, P.: Simplex meshes: a general representation for 3d shape reconstruction. Tech. Rep. (1994)
Mcinerney, T., Terzopoulos, D.: Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Transactions on Medical Imaging 18, 840–850 (1999)
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
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
Montagnat, J., Delingette, H., Ayache, N.: A review of deformable surfaces: Topology, geometry and deformation. Image and Vision Computing 19, 1023–1040 (2001)
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:10.1109/34.368173
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)
Rousson, M., Paragios, N.: Shape priors for level set representations (2004)
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
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
Sethian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge UPress (1999)
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)
Dorini, L.B.: Transformação de imagens baseadas em morfologia matemática, Ph.D. dissertation, Unicamp - Universidade Estadual de Campinas, Campinas (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sguario, M.L., de Arruda, L.V.R., Buss, I.N., Nascimento, H.C. (2014). Segmentation of Two-Phase Flow: A Free Representation for Levet Set Method with a Priori Knowledge. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-09994-1_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09993-4
Online ISBN: 978-3-319-09994-1
eBook Packages: Computer ScienceComputer Science (R0)