Abstract
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.
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
Amenta, N., Choi, S., Kolluri, R.: The power crust, unions of balls, and the medial axis transform. Computational Geometry: Theory and Applications 19(2-3), 127–153 (2001)
Bigun, J., Granlund, G.H.: Optimal orientation detection of linear symmetry. In: ICCV, pp. 433–438 (1987)
Bouix, S., Siddiqi, K.: Divergence-Based Medial Surfaces. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 603–618. Springer, Heidelberg (2000)
Bouix, S., Siddiqi, K., Tannenbaum, A.: Flux driven automatic centerline extraction. Med. Imag. Ana. 9(3), 209–221 (2005)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pat. Ana. Mach. Intel. 8, 679–698 (1986)
Chang, S.: Extracting skeletons from distance maps. Int. J. Comp. Sci. Net. Sec. 7(7) (2007)
Haralick, R.: Ridges and valleys on digital images. Comput. Vision Graph. Image Process. 22(10), 28–38 (1983)
Heimann, T., van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imag. 28(8), 1251–1265 (2009)
Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-D medial surface axis thinning algorithms. Grap. Mod. Imag. Process 56(6), 462–478 (1994)
Linguraru, M.G., Pura, J.A., Chowdhury, A.S., Summers, R.M.: Multi-organ Segmentation from Multi-phase Abdominal CT via 4D Graphs Using Enhancement, Shape and Location Optimization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 89–96. Springer, Heidelberg (2010)
Liu, X., Linguraru, M.G., Yao, J., Summers, R.M.: Organ Pose Distribution Model and an MAP Framework for Automated Abdominal Multi-Organ Localization. In: Liao, H., Edwards, P.J., Pan, X., Fan, Y., Yang, G.-Z. (eds.) MIAR 2010. LNCS, vol. 6326, pp. 393–402. Springer, Heidelberg (2010)
Lopez, A., Lumbreras, F., Serrat, J., Villanueva, J.: Evaluation of methods for ridge and valley detection. IEEE Trans. Pat. Ana. Mach. Intel. 21(4), 327–335 (1999)
Pudney, C.: Distance-ordered homotopic thinning: A skeletonization algorithm for 3D digital images. Comp. Vis. Imag. Underst. 72(2), 404–413 (1998)
Reyes, M., González Ballester, M., Li, Z., Kozic, N., Chin, S., Summers, R., Linguraru, M.: Anatomical variability of organs via principal factor analysis from the construction of an abdominal probabilistic atlas. In: IEEE Int. Symp. Biomed. Imaging, pp. 682–685 (2009)
Sabry, H.M., Farag, A.A.: Robust skeletonization using the fast marching method. In: IEEE Int. Conf. on Image Processing, vol. (2), pp. 437–440 (2005)
Siddiqi, K., Bouix, S., Tannenbaum, A., Zucker, S.W.: Hamilton-Jacobi skeletons. Int. J. Comp. Vis. 48(3), 215–231 (2002)
Styner, M., Lieberman, J.A., Pantazis, D., Gerig, G.: Boundary and medial shape analysis of the hippocampus in schizophrenia. Medical Image Analysis 8(3), 197–203 (2004)
Telea, A., van Wijk, J.J.: An augmented fast marching method for computing skeletons and centerlines. In: Symposium on Data Visualisation, VISSYM 2002, pp. 251–259. Eurographics Association (2002)
Yao, J., Summers, R.M.: Statistical Location Model for Abdominal Organ Localization. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 9–17. Springer, Heidelberg (2009)
Yushkevich, P., Zhang, H., Gee, J.: Continuous medial representation for anatomical structures. IEEE Trans. Medical Imaging 25(12), 1547–1564 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vera, S. et al. (2012). Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-28557-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28556-1
Online ISBN: 978-3-642-28557-8
eBook Packages: Computer ScienceComputer Science (R0)