This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
August 2008 (vol. 30 no. 8)
pp. 1385-1399
ASCII Text
x 
 
Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum, "A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1385-1399, August, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.70774,
author = {Samuel Dambreville and Yogesh Rathi and Allen Tannenbaum},
title = {A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {8},
issn = {0162-8828},
year = {2008},
pages = {1385-1399},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70774},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IS - 8
SN - 0162-8828
SP1385
EP1399
EPD - 1385-1399
A1 - Samuel Dambreville,
A1 - Yogesh Rathi,
A1 - Allen Tannenbaum,
PY - 2008
KW - Kernel methods
KW - shape priors
KW - active contours
KW - principal component analysisernel methods
KW - Shape
KW - priors
KW - principal component analysis
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, using level-sets. Following the work of Leventon et al., we propose to revisit the use of PCA to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, occlusions, or smearing.

[1] M. Leventon, E. Grimson, and O. Faugeras, “Statistical Shape Influence in Geodesic Active Contours,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1316-1324, 2000.
[2] A. Tsai, T. Yezzi, and W. Wells, “A Shape-Based Approach to the Segmentation of Medical Imagery Using Level Sets,” IEEE Trans. Medical Imaging, vol. 22, no. 2, pp. 137-153, 2003.
[3] A. Yezzi, S. Kichenassamy, and A. Kumar, “A Geometric Snake Model for Segmentation of Medical Imagery,” IEEE Trans. Medical Imaging, vol. 16, pp. 199-209, 1997.
[4] A. Yezzi and S. Soatto, “Deformotion: Deforming Motion, Shape Average and the Joint Registration and Approximation of Structures in Images,” Int'l J. Computer Vision, vol. 53, pp. 153-167, 2003.
[5] T. Cootes, C. Taylor, and D. Cooper, “Active Shape Models—Their Training and Application,” Computer Vision and Image Understanding, vol. 61, pp. 38-59, 1995.
[6] Y. Wang and L. Staib, “Boundary Finding with Correspondence Using Statistical Shape Models,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 338-345, 1998.
[7] D. Cremers, T. Kohlberger, and C. Schnoerr, “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional,” Int'l J. Computer Vision, vol. 50, pp. 295-313, Feb. 2002.
[8] D. Cremers, T. Kohlberger, and C. Schnoerr, “Shape Statistics in Kernel Space for Variational Image Segmentation,” Pattern Recognition, vol. 36, pp. 1292-1943, 2003.
[9] S. Mika, B. Schölkopf, A.J. Smola, K.-R. Müller, M. Scholz, and G. Rätsch, “Kernel PCA and De-Noising in Feature Spaces,” Advances in Neural Information Processing Systems, vol. 11, MIT Press, 1999.
[10] Geometric Partial Differential Equations and Image Analysis, G.Sapiro, ed. Cambridge Univ. Press, 2001.
[11] S.J. Osher and J.A. Sethian, “Fronts Propagation with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations,” J. Computational Physics, vol. 79, pp. 12-49, 1988.
[12] M. Rousson and N. Paragios, “Shape Priors for Level Set Representations,” Proc. Seventh European Conf. Computer Vision, pp. 78-92, 2002.
[13] D. Cremers, S. Osher, and S. Soatto, “Kernel Density Estimation and Intrinsic Alignment for Knowledge-Driven Segmentation: Teaching Level Sets to Walk,” Proc. 26th DAGM Pattern Recognition Symp., pp. 36-44, 2004.
[14] S. Dambreville, Y. Rathi, and A. Tannenbaum, “Shape-Based Approach to Robust Image Segmentation Using Kernel PCA,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 977-984, 2006.
[15] J.A. Sethian, Level Set Methods and Fast Marching Methods. Cambridge Univ. Press, 1999.
[16] S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces. Springer, 2003.
[17] N. Paragios, Y. Chen, and O. Faugeras, Handbook of Mathematical Models in Computer Vision. Springer, 2005.
[18] B. Schölkopf, S. Mika, and K. Müller, “Nonlinear Component Analysis as a Kernel Eigenvalue Problem,” Neural Computation, vol. 10, pp. 1299-1319, 1998.
[19] J. Kwok and I. Tsang, “The Pre-Image Problem in Kernel Methods,” IEEE Trans. Neural Networks, vol. 15, pp. 1517-1525.
[20] Y. Rathi, S. Dambreville, and A. Tannenbaum, “Comparative Analysis of Kernel Methods for Statistical Shape Learning,” Proc. Second Int'l Workshop Computer Vision Approaches to Medical Image Analysis, pp. 96-107, 2006.
[21] J. Mercer, “Functions of Positive and Negative Type and Their Connection with the Theory of Integral Equations,” Philosophical Trans. Royal Soc. of London, vol. 209, pp. 415-446, 1909.
[22] B. Schölkopf, S. Mika, and K. Müller, “An Introduction to Kernel-Based Learning Algorithms,” IEEE Trans. Neural Networks, vol. 12, pp. 181-201, 2001.
[23] A. Yezzi, A. Tsai, and A. Willsky, “A Statistical Approach to Snakes for Bimodal and Trimodal Imagery,” Proc. Int'l Conf. Computer Vision, vol. 2, pp. 898-903, 1999.
[24] T. Chan and L. Vese, “Active Contours without Edges,” IEEE Trans. Image Processing, vol. 10, no. 2, pp. 266-277, 2001.
[25] M. Rousson and R. Deriche, “A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images,” Proc. IEEE Workshop Motion and Video Computing, p. 56, 2002.
[26] N. Paragios and R. Deriche, “Geodesic Active Regions for Supervised Texture Segmentation,” Proc. Int'l Conf. Computer Vision, vol. 2, pp. 926-932, 1999.
[27] N. Paragios and R. Deriche, “Geodesic Active Regions: A New Paradigm to Deal with Frame Partition Problems in Computer Vision,” J. Visual Comm. and Image Representation, vol. 13, pp. 249-268, 2002.
[28] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic Active Contours,” Int'l J. Computer Vision, vol. 22, pp. 61-79, 1997.
[29] S. Kichenassamy, S. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, “Conformal Curvature Flow: From Phase Transitions to Active Vision,” Archives for Rational Mechanics and Analysis, vol. 134, pp. 275-301, 1996.
[30] S.C. Zhu and A.L. Yuille, “Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
[31] J.-M. Morel and S. Solimini, Variational Methods for Image Segmentation. Birkhauser, 1994.
[32] J. Kim, J. Fisher, A. Yezzi, M. Cetin, and A. Willsky, “Nonparametric Methods for Image Segmentation Using Information Theory and Curve Evolution,” Proc. Int'l Conf. Image Processing, vol. 3, pp. 797-800, 2002.
[33] Y. Rathi and O. Michailovich, “Seeing the Unseen: Segmenting with Distributions,” Proc. Int'l Conf. Signal and Image Processing, vol. 534, 2006.
[34] R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley-Interscience, 2001.
[35] Y. Rathi, S. Dambreville, and A. Tannenbaum, “Statistical Shape Analysis Using Kernel PCA,” Proc. SPIE, vol. 6064, pp. 425-432, 2006.
[36] S. Dambreville, Y. Rathi, and A. Tannenbaum, “A Shape-Based Approach to Robust Image Segmentation,” Proc. Int'l Conf. Image Analysis and Recognition, vol. 1, pp. 173-183, 2006.

Index Terms:
Kernel methods, shape priors, active contours, principal component analysisernel methods, Shape, priors, principal component analysis
Citation:
Samuel Dambreville, Yogesh Rathi, Allen Tannenbaum, "A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1385-1399, Aug. 2008, doi:10.1109/TPAMI.2007.70774
Usage of this product signifies your acceptance of the Terms of Use.