Abstract
In this paper, a fast implementation method of Chan-Vese models is proposed, which does not require numerical solutions of PDEs. The advantages of traditional level set methods, such as automatic handling of topological changes, are also preserved. The whole process is described as follows: First, the Otsu thresholding method is adopted to obtain the initial contours for the following level set evolution. Then, the initial curves are evolved to approach the true boundaries of objects by using the proposed fast implementation method of Chan-Vese model. Experimental results on some real and synthetic images show that our proposed approach is capable of automatically segmenting images with a low time-consumption.
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References
Lim, Y.W., Lee, S.U.: On the Color Image Segmentation Algorithm based on the Thresholding and the Fuzzy C-Means Techniques. Pattern Recognition 23(9), 935–952 (1990)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321–331 (1987)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. In: Proc. 5th International conf. on Computer Vision, Boston, pp. 694–699 (1995)
Osher, S., Sethian, J.A.: Fronts Propagating with Curvature-Dependent Speed: Algorithms based on Hamilton–Jacobi Formulation. Journal of Computational Physics 79, 12–49 (1988)
Tsai, Y.H.S., Osher, S.: Total Variation and Level Set Based Methods in Image Science. Acta Numerica, 1–61 (2005)
Mumford, D., Shah, J.: Optimal Approximation by Piecewise Smooth Functions and Associated Variational Problems. Commun. Pure Appl. Math. 42, 577–685 (1989)
Chan, T.F., Vese, L.A.: Active Contours without Edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)
Tsai, A., Yezzi, A., Willsky, A.S.: Curve Evolution Implementation of the Mumford–Shah Functional for Image Segmentation, Denoising, Interpolation, and Magnification. IEEE Trans. Image Process 10(8), 1169–1186 (2001)
Otsu, N.: A Threshold Selection Method from Gray Level Histogram. IEEE Transactions on System, Man and Cybernetics 8, 62–66 (1978)
Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge Monograph on Applied and Computational Mathematics. Cambridge University Press, Cambridge (1999)
Vese, L., Chan, T.: A Multiphase Level Set Framework for Image Segmentation using the Mumford and Shah Model. Inter. J. Computer Vision 50(3), 271–293 (2002)
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© 2008 Springer-Verlag Berlin Heidelberg
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Xu, H., Wang, XF. (2008). Automated Segmentation Using a Fast Implementation of the Chan-Vese Models. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_136
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DOI: https://doi.org/10.1007/978-3-540-85984-0_136
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
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