Abstract
During executing the image segmentation algorithm based on classical geometric active contour model, to obtain accurate segmentation results always involves a redundantly iterative process. And what’s more, sometimes this tedious iteration does not make the algorithm converge on the desired edge and even brings out some overshoot. To improve the segmentation efficiency and accuracy, a novel image segmentation algorithm was presented. First, the gradient image is calculated out based on the vector-valued image and then an adaptive edge indicator is proposed. Second, the revised active contour evolution model using variational level set method is put forward. The experiments demonstrate that the model has significantly increased the convergence rate and accuracy. And the proposed segmentation algorithm has also greatly improved the flexibility of the control of active contour evolution by means of its adaptive parameters adjustment.
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour model. Int. J. CV 1(4), 321–331 (1988)
Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)
Li, B., Acton, S.T.: Active contour external force using vector field convolution for image segmentation. IEEE Trans. Image Process. 16(8), 2096–2106 (2007)
Caselles, V., Catté, F., Coll, T., et al.: A geometric model for active contours in image processing. Numer. Math. 66(1), 1–31 (1993)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)
Sethian, J.A.: Level set methods and fast marching methods. J. Comput. Inf. Technol. 11(1), 1–2 (2003)
Zhao, H.-K., Chan, T., Merriman, B., et al.: A variational level set approach to multiphase motion. J. Comput. Phys. 127(1), 179–195 (1996)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Peng, D., Merriman, B., Osher, S., et al.: A PDE-based fast local level set method. J. Comput. Phys. 155(2), 410–438 (1999)
Gomes, J., Faugeras, O.: Reconciling distance functions and level sets. In: Proceedings of the 5th IEEE EMBS International Summer School on Biomedical Imaging, pp. 15–20. IEEE press, New York (2002)
Li, C., Xu, C., Gui, C., et al.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)
Chung, D.H., Sapiro, G.: On the level lines and geometry of vector-valued images. IEEE Signal Process. Lett. 7(9), 241–243 (2000)
Otsu, N.A.: Threshold selection method from gray-level histograms. Automatica 11(285), 23–27 (1975)
Acknowledgment
This material is based upon the work which is supported by the Foundation of Jimei University Li Shangda Subjects and Scientific Research Project of Educational Commission of Fujian Province in China (No. JA14175).
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Song, J., Dai, L., Wang, Y., Di Sun (2015). A Novel Image Segmentation Algorithm Based on Improved Active Contour Model. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_10
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DOI: https://doi.org/10.1007/978-3-319-23989-7_10
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