Abstract
In this paper, we introduce a semi-automated segmentation method based on minimizing the Geodesic Active Contour energy incorporating a shape prior. We increase the robustness of the segmentation result using the additional shape information that represents the desired structure. Furthermore the user has the possibility to take corrective actions during the segmentation and adapt the shape prior position. Interaction is often desirable when processing difficult data like in medical applications. To facilitate the user interaction we add a shape deformation which allows to change the shape position manually by the user and automatically in terms of underlying image features. Using a variational formulation, the optimization can be done in a globally optimal manner for a fixed shape representation. To obtain real-time behavior, which is especially important for an interactive tool, the whole method is implemented on the GPU. Experiments are done on medical, as well as on video data and camera streams that are processed in real-time. In terms of medical data we compare our method with a segmentation done by an expert. The GPU based binaries will be available online on our homepage.
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References
Cremers, D., Tischhäuser, F., Weickert, J., Schnörr, C.: Diffusion snakes: Introducing statistical shape knowledge into the Mumford–Shah functional. International Journal of Computer Vision 50(3), 295–313 (2002)
Leventon, M., Faugeraus, O., Grimson, W.: Level set based segmentation with intensity and curvature priors. In: Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 4–11 (2000)
Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 316–323. IEEE, Los Alamitos (2000)
Paragios, N., Rousson, M., Ramesh, V.: Matching distance functions: A shape-to-area variational approach for global-to-local registration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 775–789. Springer, Heidelberg (2002)
Paragios, N., Rousson, M., Ramesh, V.: Non-rigid registration using distance functions. Computer Vision and Image Understanding 89(2-3), 142–165 (2003)
Unger, M., Pock, T., Bischof, H.: Continuous Globally Optimal Image Segmentation with Local Constraints. In: Computer Vision Winter Workshop (2008)
Unger, M., Pock, T., Trobin, W., Cremers, D., Bischof, H.: TVSeg - Interactive total variation based image segmentation. In: British Machine Vision Conference (2008)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and variational problems. Comm. on Pure and Applied Math. XLII(5), 577–685 (1988)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)
Potts, R.B.: Some generalized order-disorder transformations. Proc. Camb. Phil. Soc. 48, 106–109 (1952)
Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal of Applied Mathematics 66(5), 1632–1648 (2006)
Kass, M.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1980)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A.: Conformal curvature flows: From phase transitions to active vision. Archive for Rational Mechanics and Analysis, 275–301 (1996)
Kichenassamy, S., Kumar, A., Olver, P.J., Tannenbaum, A.R., Yezzi Jr., A.J.: Gradient flows and geometric active contour models. In: International Conference on Computer Vision, pp. 810–815 (1995)
Leung, S., Osher, S.: Global minimization of the active contour model with TV-inpainting and two-phase denoising. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds.) VLSM 2005. LNCS, vol. 3752, pp. 149–160. Springer, Heidelberg (2005)
Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J.P., Osher, S.J.: Global minimizers of the active contour/snake model. In: International Conference on Free Boundary Problems: Theory and Applications (FBP) (2005)
Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J.P., Osher, S.J.: Fast global minimization of the active contour/snake model. J. of Mathematical Imaging and Vision 28(2), 151–167 (2007)
Chan, T.F., Esedoglu, S.: Aspects of total variation regularized L1 function approximation. SIAM Journal of Applied Mathematics 65(5), 1817–1837 (2005)
Cremers, D., Schmidt, F.R., Barthel, F.: Shape priors in variational image segmentation: Convexity, Lipschitz continuity and globally optimal solutions. In: Computer Vision and Pattern Recognition, pp. 1–6 (2008)
Chan, T.F., Golub, G.H., Mulet, P.: A nonlinear primal-dual method for total variation-based image restoration. SIAM Journal on Scientific Computing 20(6), 1964–1977 (1999)
Carter, J.: Dual Methods for Total Variation-based Image Restoration. PhD thesis, UCLA (2001)
Chambolle, A.: An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision 20(1-2), 89–97 (2004)
Chambolle, A.: Total variation minimization and a class of binary MRF models. In: Rangarajan, A., Vemuri, B.C., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 136–152. Springer, Heidelberg (2005)
Rudin, L.I., Osher, S.J., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60, 259–268 (1992)
Zhu, M., Chan, T.: An efficient primal-dual hybrid gradient algorithm for total variation image restoration. UCLA CAM Report 08-34 (2008)
Zhu, M., Wright, S.J., Chan, T.F.: Duality-based algorithms for total variation image restoration. UCLA CAM Report 08-33 (2008)
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Werlberger, M., Pock, T., Unger, M., Bischof, H. (2009). A Variational Model for Interactive Shape Prior Segmentation and Real-Time Tracking. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_17
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DOI: https://doi.org/10.1007/978-3-642-02256-2_17
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