Abstract
To get better segmentation results, local information and global information should be taken into consideration together. In this paper, we propose a new energy functional which combines a local intensity fitting term and an auxiliary global intensity fitting term, and we also give the method to adjust the weight of auxiliary global fitting term dynamically by using local contrast of the image. The combination of the two terms improves the accuracy of segmentation results obviously while reduces dependence on location of initial contour. The experiment results proved the effectiveness of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, C., Kao, C., Gore, J., Ding, Z.: Implicit active contours driven by local binary fitting energy. In: CVPR 2007, pp. 1–7 (2007)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contour. IJCV 22(1), 61–79 (1997)
Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE T-IP 7(3), 359–369 (1998)
Paragios, N., Mellina-Gottardo, O., Ramesh, V.: Gradient vector flow geometric active contours. IEEE T-PAMI 26(3), 402–407 (2004)
Li, C., Liu, J., Fox, M.: Segmentation of edge preserving gradient vector flow: an approach toward automatically initializing and splitting of snakes. In: CVPR 2005, pp. 162–167 (2005)
Chan, T., Vese, L.: Active contours without edges. IEEE T-IP 10(2), 266–277 (2001)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. IJCV 46(3), 223–247 (2002)
Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. IJCV 72(2), 195–215 (2007)
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: CVPR, vol. 1, pp. 430–436 (2005)
Li, W., Li, C., Sun, Q., Xia, D., Kao, C.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Computerized Medical Imaging and Graphics 33, 520–531 (2009)
Mumford, D., Shah, J.: Optimal approximation by piecewise smooth function and associated variational problems. Communication on Pure and Applied Mathematics 42, 577–685 (1989)
Osher, S., Sethian, J.A.: Fronts Propagating with Curvature dependent Speed: Algorithms Based on Hamilton-Jacobi Formulation. Journal Computational Physics 79, 12–49 (1988)
Vese, L., Chan, T.A.: Multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vision 50, 271–293 (2002)
Tsai, A., Yezzi, A., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transaction on Image Processing 10, 1169–1186 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, Y., Zhang, C., Wei, Y., Li, X. (2010). Active Contour Method Combining Local Fitting Energy and Global Fitting Energy Dynamically. In: Zhang, D., Sonka, M. (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13923-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-13923-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13922-2
Online ISBN: 978-3-642-13923-9
eBook Packages: Computer ScienceComputer Science (R0)