Abstract
We propose a novel segmentation approach for introducing shape priors in the geometric active contour framework. Following the work of Leventon, we propose to revisit the use of linear principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. Our contribution in this paper is twofold. First, we demonstrate that building a space of familiar shapes by applying PCA on binary images (instead of signed distance functions) enables one to constrain the contour evolution in a way that is more faithful to the elements of a training set. Secondly, we present a novel region-based segmentation framework, able to separate regions of different intensities in an image. Shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows for the simultaneous encoding of multiple types of shapes and leads to promising segmentation results. In particular, our shape-driven segmentation technique offers a convincing level of robustness with respect to noise, clutter, partial occlusions, and blurring.
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References
Leventon, M., Grimson, E., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. CVPR, pp. 1316–1324. IEEE, Los Alamitos (2000)
Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. Transactions on Pattern analysis and Machine Intelligence 22, 266–280 (2000)
Tsai, A., Yezzi, T., Wells, W., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. on Medical Imaging 22, 137–153 (2003)
Yezzi, A., Kichenassamy, S., Kumar, A., et al.: A geometric snake model for segmentation of medical imagery. IEEE Trans. Medical Imag. 16, 199–209 (1997)
Yezzi, A., Soatto, S.: Deformotion: Deforming motion, shape average and the joint registration and approximation of structures in images. International Journal of Computer Vision 53, 153–167 (2003)
Blake, A., Isard, M. (eds.): Active Contours. Springer, Heidelberg (1998)
Cootes, T., Taylor, C., Cooper, D., et al.: Active shape models-their training and application. Comput. Vis. Image Understanding 61, 38–59 (1995)
Wang, Y., Staib, L.: Boundary finding with correspondance using statistical shape models. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 338–345 (1998)
Cremers, D., Kohlberger, T., Schnoerr, C.: Diffusion snakes: introducing statistical shape knowledge into the mumford-shah functional. International journal of computer vision 50 (2002)
Cremers, D., Kohlberger, T., Schnoerr, C.: Shape statistics in kernel space for variational image segmentation. Pattern Recognition 36, 1292–1943 (2003)
Mika, S., Scholkopf, B., Smola, A., et al.: Kernel pca and de-noising in feature spaces. In: Advances in neural information processing systems, vol. 11 (1998)
Sapiro, G.: Geometric Partial Differential Equations and Image Analysis. Cambridge University Press, Cambridge (2001)
Osher, S., Sethian, J.: Fronts propagation with curvature dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)
Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)
Yezzi, A., Tsai, A., Willsky, A.: A statistical approach to snakes for bimodal and trimodal imagery. In: Proc. Int. Conf. Computer Vision, vol. 2, pp. 898–903 (1999)
Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10, 266–277 (2001)
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Dambreville, S., Rathi, Y., Tannenbaum, A. (2006). A Shape-Based Approach to Robust Image Segmentation. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_17
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DOI: https://doi.org/10.1007/11867586_17
Publisher Name: Springer, Berlin, Heidelberg
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