Abstract
This paper presents a novel efficient multiscale vessel segmentation method using the level-set framework. This technique is based on the active contour model that evolves according to the geometric measure of vessel structures. Inspired by the multiscale vessel enhancement filtering, the prior knowledge about the vessel shape is incorporated into the energy function as a region information term. In this method, a new region-based external force is combined with existing geometric snake variation models. A new speed function is designed to precisely control the curve deformation. This multiscale method is more efficient for the segmentation of vessel and line-like structures than the conventional active contour methods. Furthermore, the whole model is implemented in a level-set framework. The solution is stable and robust for various topologic changes. This method was compared with other geometric active contour models. Experimental results of human lung CT images show that this multiscale method is accurate.
Keywords
- Active Contour
- Active Contour Model
- Speed Function
- Vessel Segmentation
- Geodesic Active Contour
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.
Chapter PDF
References
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two dimensional matched filters. IEEE Transactions on Medical Imaging 8, 13–18 (1989)
Thackray, B.D., Nelson, A.C.: Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Transactions on Medical Imaging 12, 3–22 (1993)
McInerney, T., Terzopou los, D.: Topology adaptive snakes. Medical Image Analysis 4, 73–91 (2000)
Sechian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Transact ions on Pattern Analysis and Machine Intelligence 17, 2–25 (1995)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Nikos, P.: Geodesic active regions: a new framework to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation 13, 249–268 (2002)
Yezzi Jr., A., Andy, T., Alan, W.: A fully global approach to image segmentation via coupled curve evolution equations. Journal of Visual Communication and Image Representation 13, 195–216 (2002)
Pascal, M., Philippe, R., Francois, G., Prederic, G.: Influence of the noise model on level set active contour segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 799–803 (2004)
Ali, G., Raphael, C.: A new fast level set method. In: Proceedings of the 6 the Signal Processing Symposium, pp. 232–235 (2004)
Caselles, V., Kimmel, R., Spairo, G.: Geodesic active contours. International journal of Computer Vision 22, 61–79 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, G., Miao, Y., Li, P., Bian, Z. (2005). Multiscale Vessel Segmentation: A Level Set Approach. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_73
Download citation
DOI: https://doi.org/10.1007/11578079_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
Publish with us
-
Published in cooperation with
http://www.iapr.org/
