Abstract
In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were first segmented manually. The resulting structures were represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between the different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result of the segmentation is promising, but the quality of the segmentation is dependent on the initial guess.
Chapter PDF
Similar content being viewed by others
References
Cootes, T., Taylor, C.: Statistical models of appearance for computer vision (2004), http://www.isbe.man.ac.uk/~bim/Models/app_models.pdf
Zhao, H.K., Osher, S., Merriman, B., Kang, M.: Implicit and nonparametric shape reconstruction from unorganized data using a variational level set method. Computer Vision and Image Understanding 80, 295–314 (2000)
Shi, Y., Karl, W.: Shape reconstruction from unorganized points with a data-driven level set method. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004. Proceedings (ICASSP 2004), vol. 3, pp. 13–16 (2004)
Adalsteinsson, D., Sethian, J.: The fast construction of extension velocities in level set methods. Journal of Computational Physics 148, 2–22 (1999)
Zhao, H.K., Chan, T., Merriman, B., Osher, S.: A variational level set approach to multiphase motion. Journal of Computational Physics 127, 179–195 (1996)
Besl, P., McKay, H.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(91), 376–380
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
Josephson, K., Ericsson, A., Karlsson, J. (2005). Segmentation of Medical Images Using Three-Dimensional Active Shape Models. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds) Image Analysis. SCIA 2005. Lecture Notes in Computer Science, vol 3540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499145_73
Download citation
DOI: https://doi.org/10.1007/11499145_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26320-3
Online ISBN: 978-3-540-31566-7
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
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.
