Abstract
This paper presents the creation of 3D statistical shape models of the knee bones and their use to embed information into a segmentation system for MRIs of the knee. We propose utilising the strong spatial relationship between the cartilages and the bones in the knee by embedding this information into the created models. This information can then be used to automate the initialisation of segmentation algorithms for the cartilages. The approach used to automatically generate the 3D statistical shape models of the bones is based on the point distribution model optimisation framework of Davies. Our implementation of this scheme uses a parameterized surface extraction algorithm, which is used as the basis for the optimisation scheme that automatically creates the 3D statistical shape models. The current approach is illustrated by generating 3D statistical shape models of the patella, tibia and femoral bones from a segmented database of the knee. The use of these models to embed spatial relationship information to aid in the automation of segmentation algorithms for the cartilages is then illustrated.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hargreaves, B.A., Gold, G.E., Beaulieu, C.F., Vasanawala, S.S., Nishimura, D.G., Pauly, J.M.: Comparison of new sequences for high resolution cartilage imaging. Magnetic Resonance in Medicine 49, 700–709 (2003)
Cohen, Z.A., McCarthy, D.M., Kwak, S.D., Legrand, P., Fogarasi, F., Ciaccio, E.J., Ateshian, G.A.: Knee cartilage topology, thickness and contact areas from MRI: in-vitro calibration and in-vivo measurements. Osteoarthritis Cartilage 7, 95–109 (1999)
Williams, T.G., Taylor, C.J., Gao, Z., Waterton, J.C.: Corresponding articular cartilage thickness measurements in the knee joint by modelling the underlying bone. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 480–487. Springer, Heidelberg (2003)
Wluka, A.E., Stuckey, A., Snaddon, J., Cicuttini, F.: The determinants of change in tibial cartilage volume in osteoarthritic knees. Arthritis and Rheumatism 46, 2065–2072 (2002)
Hohe, J., Ateshian, G., Reiser, M., Englmeier, K.H., Eckson, F.: Surface size, curvature analysis and assessment of knee joint incongruity with MRI in vivo. Magnetic Resonance in Medicine 47, 554–561 (2002)
Eckstein, F., Schnier, M., Haubner, M., Priebsch, J., Glaser, C., Englmeier, K.H., Reiser, M.: Accuracy of cartilage volume and thickness measurements with magnetic resonance imaging. Clinical Orthopaedics and related research 352, 137–148 (1998)
Solloway, S., Hutchinson, C., Waterton, J., Taylor, C.: The use of active shape models for making thickness measurements of articular cartilage from MR images. Magnetic Resonance in Medicine 37, 943–952 (1997)
Lynch, J., Zaim, S., Zhao, J., Stork, A., Peterfly, C.G., Genant, H.: Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours. SPIE 3979, 925–935 (2000)
Gougoutas, A., Wheaton, A., Borthakur, A., Shapiro, E., Kneeland, J., Udupa, J., Reddy, R.: Cartilage volume quantification via live wire segmentation. Academic Radiology 11, 1389–1395 (2004)
Kapur, T., Beardsley, P., Gibson, S., Grimson, W., Wells, W.: Model-based segmentation of clinical knee MRI. In: Proc. IEEE Int’l Workshop on Model-Based 3D Image Analysis, pp. 97–106 (1998)
Ghosh, S., Beuf, O., Ries, M., Lane, N., Steinbach, L.S., Majumdar, S.: Watershed segmentation of high resolution magnetic resonance images of articular cartilage of the knee. Engineering in Medicine and Biology Society 4, 3174–3176 (2000)
Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification. Medical Image Analysis 4, 43–55 (2000)
Grau, V., Mewes, A., Alcaniz, M., Kikinis, R., Warfield, S.: Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging 23, 447–458 (2004)
Hamarneh, G., McInerney, T., Terpzopoulos, D.: Intelligent deformable organisms: An artificial life approach to medical image analysis. Technical report CSRG-432 (2001)
Davies, R., Twining, C., Cootes, T., Waterton, J., Taylor, C.: 3D statistical shape models using direct optimisation of description length. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 3–21. Springer, Heidelberg (2002)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Undertanding 61, 38–59 (1995)
Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3d statistical shape models using non-rigid registration. IEEE Transactions on Medical Imaging 22, 1014–1025 (2003)
Siers, M., Frangi, A., Kaus, M., Niessen, W.: Comparison of two 3D automatic landmarking methods for a large training set of cardiac MR images (citeseer), citeseer.ist.psu.edu/529161.html
Kobbeltm, L., Vorsatz, J., Labsik, U., Seidel, H.: A Shrink Wrapping Approach to Remeshing Polygonal Surfaces. In: Proceedings of the 20th Annual Conference ot the European Association of Computer Graphics (Eurographics 1999) (1999)
Lee, S.L., Horkaew, P., Darzi, A., Yang, G.Z.: Statistical Shape Modelling of the Levator Ani with Thickness Variation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 258–265. Springer, Heidelberg (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
Fripp, J., Bourgeat, P., Mewes, A.J.U., Warfield, S.K., Crozier, S., Ourselin, S. (2005). 3D Statistical Shape Models to Embed Spatial Relationship Information. 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_7
Download citation
DOI: https://doi.org/10.1007/11569541_7
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)