Abstract
We propose a Bayesian formulation for coupled surface evolutions and apply it to the segmentation of the prostate and the bladder in CT images. This is of great interest to the radiotherapy treatment process, where an accurate contouring of the prostate and its neighboring organs is needed. A purely data based approach fails, because the prostate boundary is only partially visible. To resolve this issue, we define a Bayesian framework to impose a shape constraint on the prostate, while coupling its extraction with that of the bladder. Constraining the segmentation process makes the extraction of both organs’ shapes more stable and more accurate. We present some qualitative and quantitative results on a few data sets, validating the performance of the approach.
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
Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: Teaching level sets to walk. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 36–44. Springer, Heidelberg (2004)
Cremers, D., Rousson, M.: Efficient kernel density estimation of shape and intensity priors for level set segmentation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 757–764. Springer, Heidelberg (2005)
Dam, E.B., Fletcher, P.T., Pizer, S., Tracton, G., Rosenman, J.: Prostate shape modeling based on principal geodesic analysis bootstrapping. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 1008–1016. Springer, Heidelberg (2004)
Freedman, D., Radke, R.J., Zhang, T., Jeong, Y., Lovelock, D.M., Chen, G.T.: Model-based segmentation of medical imagery by matching distributions. IEEE Trans Med Imaging 24(3), 281–292 (2005)
Leventon, M., Grimson, E., Faugeras, O.: Statistical Shape Influence in Geodesic Active Contours. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, June 2000, pp. 316–323. Hilton Head Island, South Carolina (2000)
Osher, S., Sethian, J.: Fronts propagating with curvature dependent speed: algorithms based on the Hamilton–Jacobi formulation. J. of Comp. Phys. 79, 12–49 (1988)
Paragios, N., Deriche, R.: Geodesic active regions: a new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, Special Issue on Partial Differential Equations in Image Processing, Computer Vision and Computer Graphics 13(1/2), 249–268 (2002)
Rousson, M., Paragios, N., Deriche, R.: Implicit active shape models for 3d segmentation in mr imaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 209–216. Springer, Heidelberg (2004)
Tsai, A., Wells, W., Tempany, C., Grimson, E., Willsky, A.: Mutual information in coupled multi-shape model for medical image segmentation. Medical Image Analysis 8(4), 429–445 (2004)
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
Rousson, M., Khamene, A., Diallo, M., Celi, J.C., Sauer, F. (2005). Constrained Surface Evolutions for Prostate and Bladder Segmentation in CT Images. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_26
Download citation
DOI: https://doi.org/10.1007/11569541_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
Online ISBN: 978-3-540-32125-5
eBook Packages: Computer ScienceComputer Science (R0)