Abstract
This paper proposes a novel framework for image segmentation through a unified model-based and pixel-driven integrated graphical model. Prior knowledge is expressed through the deformation of a discrete model that consists of decomposing the shape of interest into a set of higher order cliques (triplets). Such decomposition allows the introduction of region-driven image statistics as well as pose-invariant (i.e. translation, rotation and scale) constraints whose accumulation introduces global deformation constraints on the model. Regional triangles are associated with pixels labeling which aims to create consistency between the model and the image space. The proposed formulation is pose-invariant, can integrate regional statistics in a natural and efficient manner while being able to produce solutions unobserved during training. The challenging problem of tagged cardiac MR image segmentation is used to demonstrate the performance potentials of the method.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Besbes, A., Komodakis, N., Langs, G., Paragios, N.: Shape priors and discrete mrfs for knowledge-based segmentation. In: CVPR, pp. 1295–1302 (2009)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. IJCV 70, 109–131 (2006)
Bray, M., Kohli, P., Torr, P.: PoseCut: Simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 642–655. Springer, Heidelberg (2006)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. PAMI 24, 603–619 (2002)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. PAMI 23(6), 681–685 (2001)
Cootes, T., Taylor, C., Cooper, D., Graham, J., et al.: Active shape models-their training and application. CVIU 61, 38–59 (1995)
Glocker, B., Komodakis, N., Paragios, N., Glaser, C., Tziritas, G., Navab, N.: Primal/dual linear programming and statistical atlases for cartilage segmentation. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 536–543. Springer, Heidelberg (2007)
Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. PAMI 28(10), 1568–1583 (2006)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22 (2000)
Staib, L., Duncan, J.: Boundary finding with parametrically deformable models. PAMI 14(11), 1061–1075 (1992)
Taron, M., Paragios, N., Jolly, M.: Registration with uncertainties and statistical modeling of shapes with variable metric kernels. PAMI 31(1), 99–113 (2009)
Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the mumford and shah model. IJCV 50, 271–293 (2002)
Wang, C., de La Gorce, M., Paragios, N.: Segmentation, ordering and multi-object tracking using graphical models. In: ICCV, pp. 747–754 (2009)
Xiang, B., Wang, C., Deux, J., Rahmouni, A., Paragios, N.: 3d cardiac segmentation with pose-invariant higher-order mrfs. In: ISBI, pp. 1425–1428 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiang, B., Deux, JF., Rahmouni, A., Paragios, N. (2013). Joint Model-Pixel Segmentation with Pose-Invariant Deformable Graph-Priors. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)