Abstract
In this paper, we present an improved region-based active contour/surface model for 2D/3D brain MR image segmentation. Our model combines the advantages of both local and global intensity information, which enable the model to cope with intensity inhomogeneity. We define an energy functional with a local intensity fitting term and an auxiliary global intensity fitting term. In the associated curve evolution, the motion of the contour is driven by a local intensity fitting force and a global intensity fitting force, induced by the local and global terms in the proposed energy functional, respectively. The influence of these two forces on the curve evolution is complementary. When the contour is close to object boundaries, the local intensity fitting force became dominant, which attracts the contour toward object boundaries and finally stops the contour there. The global intensity fitting force is dominant when the contour is far away from object boundaries, and it allows more flexible initialization of contours by using global image information. The proposed model has been applied to both 2D and 3D brain MR image segmentation with promising results.
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Keywords
- Brain Magnetic Resonance Image
- Active Contour
- Object Boundary
- Active Contour Model
- Intensity Inhomogeneity
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int’l J. Comp. Vis. 1, 321–331 (1987)
Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66, 1–31 (1993)
Kimmel, R., Amir, A., Bruckstein, A.: Finding shortest paths on surfaces using level set propagation. IEEE Trans. Patt. Anal. Mach. Intell. 17, 635–640 (1995)
Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Imag. Proc. 7(3), 359–369 (1998)
Ronfard, R.: Region-based strategies for active contour models. Int’l. J. Comp. Vis. 13, 229–251 (1994)
Samson, C., Blanc-Feraud, L., Aubert, G., Zerubia, J.: A variational model for image classification and restoration. IEEE Trans. Patt. Anal. Mach. Intell. 22(5), 460–472 (2000)
Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Imag. Proc. 10, 266–277 (2001)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int’l. J. Comp. Vis. 46, 223–247 (2002)
Rousson, M., Cremers, D.: Implicit active shape models for 3D segmentation in MR imaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 209–216. Springer, Heidelberg (2004)
Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int’l. J. Comp. Vis. 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 Trans. Imag. Proc. 10, 1169–1186 (2001)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)
Piovano, J., Rousson, M., Papadopoulo, T.: Efficient segmentation of piecewise smooth images. In: Scale Space and Variational Methods in Computer Vision, pp. 709–720 (2007)
Li, C., Kao, C., Gore, J., Ding, Z.: Implicit active contours driven by local binary fitting energy. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR) (2007)
Li, C., Kao, C., Gore, J., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Imag. Proc. (to appear)
Brox, T., Cremers, D.: On the statistical interpretation of the piecewise smooth Mumford-Shah functional. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 203–213. Springer, Heidelberg (2007)
Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: A new variational formulation. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), vol. 1, pp. 430–436 (2005)
Wells, W., Grimson, E., Kikinis, R., Jolesz, F.: Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 15(4), 429–442 (1996)
Jaccard, P.: The distribution of flora in the alpine zone. New Phytol. 11(2), 37–50 (1912)
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Wang, L., Li, C., Sun, Q., Xia, D., Kao, CY. (2008). Brain MR Image Segmentation Using Local and Global Intensity Fitting Active Contours/Surfaces. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_46
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DOI: https://doi.org/10.1007/978-3-540-85988-8_46
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