Abstract
A local deformable-model-based segmentation can be very helpful to extract objects from an image, especially when no prototype about the object is available. However, this technique can drive to an erroneous segmentation in noisy images, in case of the active contour is captured by noise particles. If some geometrical information (a priori knowledge) of the object is available, then it can be used to validate the results and to obtain a better segmentation through a refining stage. A Bayesian approach is proposed to evaluate the solution of segmentation performed by a deformable model. The proposed framework is validated for vessel segmentation on mammograms. For that purpose a specific geometric restriction term for vessels on mammograms is formulated. Also a likelihood function of the contour and the image is developed. Our model avoids manual initialization of the contour using a local deformable model to obtain an initial contour approximation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Choi, Wai-Pak; Lam, Kin-Man, and Siu, Wan-Chi. An adaptive active contour model for highly irregular boundaries. Pattern Recognition. 2001; vol 34(2):323-331.
Valverde, F. L.; Guil, N., and Munoz, J. A deformable model for image segmentation in noisy medical images. IEEE International Conference on Image Processing, ICIP 2001. 2001:82–85.
Cheung, K.; Yeung, D., and Chin, R. On deformable models for visual pattern recognition. Pattern Recognition. 2002; vol 35:1507–1526.
Kass, M.; Witkin, A., and Terzopoulos, D. Snakes: Active Contour Models. International Journal of Computer Vision. 1987:321–331.
Wang H. and Gosh, B. Geometric active deformable models in shape modelling. IEEE Transactions on Image Processing. 2000; vol 9(2):302–307.
Rueckert, D.; Burger, P.; Forbat, S. M., and Mohiaddin, R. D. Automatic traking of the aorta in cardiovascular mr images using deformable models. IEEE Transactions on Med Imaging. 1997; vol 16(5):581–590.
Delibasis, H and Undrill, P. E. Anatomical object recognition using deformable geometric models. Image and Vision Computing. 1994; vol 12(7):423–433.
Clarysse, P; Friboulet, D, and Magnin, I. E. Tracking geometrical descriptors on 3-D deformable surfaces-aplication to the left ventricular surface of the heart. IEEE Transactions on Med Imaging. 1997; vol 16(4):392–404.
Shen, D. and Davatzikos, C. An adaptative-Focus deformable model using statisitical and geometric information. IEEE Transactions on Patterns Analysis and Machine Intelligence. 2000; vol 22(8):906–912.
Amini, A. A.; Weymouth, T. E., and Jain, R. C. Using dynamic programming for solving variational problems in vision. IEEE Transactions on Patterns Analysis and Machine Intelligence. 1990 Sep; vol 12(9):855–866.
Williams, Donna J. and Shah, Mubarak. A fast algorithm for active Contours and Curvature Estimation. Computer Vision, Graphics and Image Processing. 1992; vol 55(1):14–26.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Valverde, F.L., Guil, N., Muñoz, J. (2002). Bayesian Approach Based on Geometrical Features for Validation and Tuning of Solution in Deformable Models. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_88
Download citation
DOI: https://doi.org/10.1007/3-540-36131-6_88
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00131-7
Online ISBN: 978-3-540-36131-2
eBook Packages: Springer Book Archive