INTERPOLATION SNAKES FOR BORDER DETECTION IN ULTRASOUND IMAGES
Silviu Minut, George Stockman
2006
Abstract
Ultrasound images present major challanges to just about any segmentation algorithm, including active contour techniques, due to increased specularity, non-uniform edges along the boundaries of interest, incomplete and misleading visual support. Active contours that depend on a vector of parameters (e.g. B-splines), have been proposed in the literature, and have the advantage over traditional snakes and level-set snakes, that smoothness is built-in, which is a sine qua non requirement in border detection in medical images. We propose in this paper the use of interpolation splines as active contours for border detection in ultrasound images, which we term interpolation snakes. We argue that interpolation snakes are better suited for ultrasound than other snakes, because of the fact that the control points (parameters which control the shape of the snake) are on the curve. This allows for an initial arclength parameterization of the snake. In conjunction with interpolation snakes we define a new energy (measure of fit) which incorporates a term supposed to maintain arclength parameterization of the snake throughout the minimization process. A shape prior can also be introduced naturally, as a distribution on the control points.
References
- Adalsteinsson, D. and Sethian, J. A. (1995). A fast level set method for advancing interfaces. Journal of Computational Physics, 118(2):269-277.
- Amini, A. A., Weymouth, T. T., and Jain, R. C. (1990).
- Bascle, B. and Deriche, R. (1992). Feature extraction using parametric snakes. In Proceedings of the 11-th IAPR International Conference on Pattern Recognition, pages 659-663, Netherlands.
- Brigger, P., Hoeg, J., and Unser, M. (2000). B-spline snakes: A flexible tool for parametric contour detection. IEEE Transactions on Image Processing, 9(9):1484-1496.
- Caselles, V., Catte, F., Coll, T., and Dibos, F. (1993). A geometric model for active contours. Numerische Mathematik, 66:1-31.
- Caselles, V., Kimmel, R., and Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1):61-79.
- Cohen, L. (1991). On active contour models and balloons. CVGIP: Image Understanding, 53(2):211-218.
- Cootes, T., Taylor, C. J., Cooper, D. H., and Graham, J. (1995). Active shape models - their training and application. Computer Vision and Image Understanding, 61(1):38-59.
- Crandall, M. and Lions, P. (1984). Two approximations of solutions to Hamilton-Jacobi equations. Mathematics of Computation, 43(167):1-19.
- Cremers, D., Kohlberger, T., and Schnorr, C. (2003). Shape statistics in kernel space for variational image segmentation. Pattern Recognition, 36(9):1929-1943.
- Cremers, D., Osher, S., and Soatto, S. (2004). Kernel density estimation and intrinsic alignment for knowledgedriven segmentation: Teaching level sets to walk. Pattern Recognition, 3175:36-44.
- de Boor, C. (2001). A Practical Guide to Splines. Springer.
- Figueredo, M., Leitao, J., and Jain, A. (2000). Unsupervised contour representation and estimation using bsplines and a minimum description length criterion. IEEE Transactions of Image Processing, 9(6):1075- 1087.
- Jain, A., Zhong, Y., and Lakshmanan, S. (96). Object matching using deformable templates. IEEE PAMI., 18(3):267-278.
- Kass, M., Witkin, A. P., and Terzopoulos, D. (1988). Snakes: Active contour models. IJCV, 1(4):321-331.
- Kitware (2005). The Insight Segmentation and Registration Toolkit (ITK). http://www.itk.org.
- Leventon, M. E., Grimson, W. E. L., and Faugeras, O. (2000). Statistical shape influence in geodesic active contours. In IEEE Proceedings of CVPR, volume 1, pages 316-323, Hilton Head Island, North Carolina.
- Malladi, R., Sethian, J., and Vemuri, B. (1995). Shape modeling with front propagation: A level set approach. PAMI, 17(2):158-175.
- McInerney, T. and Terzopoulos, D. (2000). Topology adaptive snakes. Medical Image Analysis, 4:73-91.
- Menet, S., Saint-Marc, P., and Medioni, G. (1990). Bsnakes: Implementation and application to stereo. In DARPA90, pages 720-726.
- Osher, S. and Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79:12-49.
- Osher, S. and Sethian, J. A. (1990). Recent numerical algorithms for hypersurfaces moving with curvaturedependent speed: Hamilton-Jacobi equations and conservation laws. Journal of Differential Geometry, 31, pp. 131-161, 1990, 31:131-161.
- Osher, S. and Shu, C.-W. (1991). High-order essentially non oscillatory schemes for Hamilton-Jacobi equations. Siam Journal of Numerical Analysis, 28(4):907-922.
- Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. (1993). Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 2nd edition edition. available online at http://www.nr.com.
- Rueckert, D. (1997). Segmentation and Tracking in Cardiovascular MR Images Using Geometrically Deformable Models and Templates. PhD thesis, Department of Computing, Imperial College of Science, Technology and Medicine, London.
- Sethian, J. (2001). Level Set Methods and Fast Marching Methods. Cambridge Monographs on Applied and Computational Mathematics. Cambridge University Press, 2 edition.
- Sethian, J. A. (1996). A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. USA, 93:1591-1595.
- Staib, L. and Duncan, J. S. (1992). Boundary finding with parametrically deformable models. PAMI, 14(11):1061-1075.
- Viola, P. and Jones, M. (2004). Robust real-time face detection. IJCV, 57(2):137-154.
- Williams, D. J. and Shah, M. (1992). A fast algorithm for active contours and curvature estimation. CVGIP: Image Underst., 55(1):14-26.
- Xu, C. and Prince, J. (1997). Gradient vector flow: A new external force for snakes. In CVPR97, pages 66-71.
Paper Citation
in Harvard Style
Minut S. and Stockman G. (2006). INTERPOLATION SNAKES FOR BORDER DETECTION IN ULTRASOUND IMAGES . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 297-305. DOI: 10.5220/0001364202970305
in Bibtex Style
@conference{visapp06,
author={Silviu Minut and George Stockman},
title={INTERPOLATION SNAKES FOR BORDER DETECTION IN ULTRASOUND IMAGES},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={297-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001364202970305},
isbn={972-8865-40-6},
}
in EndNote Style
TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - INTERPOLATION SNAKES FOR BORDER DETECTION IN ULTRASOUND IMAGES
SN - 972-8865-40-6
AU - Minut S.
AU - Stockman G.
PY - 2006
SP - 297
EP - 305
DO - 10.5220/0001364202970305