Abstract
The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure.
We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.
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
Peng, Z., Zhong, J., Wee, W., Lee, J.h.: Automated vertebra detection and segmentation from the whole spine MR images. In: Proc. Engineering in Medicine and Biology, IEEE-EMBS 2005, pp. 2527–2530 (2005)
Weiss, K.L., Storrs, J.M., Banto, R.B.: Automated spine survey iterative scan technique. Radiology 239(1), 255–262 (2006)
Vrtovec, T., Ourselin, S., Gomes, L., Likar, B., Pernus, F.: Generation of curved planar reformations from magnetic resonance images of the spine. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 135–143. Springer, Heidelberg (2006)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models – their training and application. Comp. Vision Image Underst. 61(1), 38–59 (1995)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision 60, 135–164 (2004)
Abi-Nahed, J., Jolly, M.P., Yang, G.Z.: Robust active shape models: A robust, generic and simple automatic segmentation tool. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 1–8. Springer, Heidelberg (2006)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61, 55–79 (2005)
Pham, T.V., Smeulders, A.W.M.: Object recognition with uncertain geometry and uncertain part detection. Computer Vision and Image Understanding 99(2), 241–258 (2005)
Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: ECCV (1). pp. 18–32 (2000)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). vol. 2, p. 264 (2003)
Amit, Y.: Graphical shape templates for automatic anatomy detection with applications to MRI brain scans. IEEE Trans. Med. Imaging 16(1), 28–40 (1997)
Bergtholdt, M., Kappes, J.H., Schnörr, C.: Learning of graphical models and efficient inference for object class recognition. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) Pattern Recognition. LNCS, vol. 4174, pp. 273–283. Springer, Heidelberg (2006)
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1465–1479 (2006)
Minoux, M.: Mathematical programming: theory and algorithms. Wiley, Chichester (1986)
Pakzad, P., Anantharam, V.: Estimation and marginalization using Kikuchi approximation methods. Neural Computation 17(8), 1836–1873 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Schmidt, S. et al. (2007). Spine Detection and Labeling Using a Parts-Based Graphical Model. In: Karssemeijer, N., Lelieveldt, B. (eds) Information Processing in Medical Imaging. IPMI 2007. Lecture Notes in Computer Science, vol 4584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73273-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-73273-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73272-3
Online ISBN: 978-3-540-73273-0
eBook Packages: Computer ScienceComputer Science (R0)