Abstract
Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.
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
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)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–684 (2001)
Joshi, S., Pizer, S., Fletcher, P., Yushkevich, P., Thall, A., Marron, J.: Multiscale deformable model segmentation and statistical shape analysis using medial descriptions. IEEE Transactions on Medical Imaging 21(5), 538–550 (2002)
Duta, N., Sonka, M.: Segmentation and interpretation of MR brain images: An improved active shape model. IEEE Transactions on Medical Imaging 17(6), 1049–1067 (1998)
van Ginneken, B., Frangi, A., Staal, J., ter Haar Romeny, B., Viergever, M.: Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging 21(8), 924–933 (2002)
Behiels, G., Maes, F., Vandermeulen, D., Suetens, P.: Evaluation of image features and search strategies for segmentation of bone structures in radiographs using active shape models. Medical Image Analysis 6(1), 47–62 (2002)
de Bruijne, M., van Ginneken, B., Niessen, W., Maintz, J., Viergever, M.: Active shape model based segmentation of abdominal aortic aneurysms in CTA images. In: Sonka, M., Fitzpatrick, M. (eds.) Medical Imaging: Image Processing, Proceedings of SPIE, vol. 4684, pp. 463–474. SPIE Press, Bellingham (2002)
Rogers, M., Graham, J.: Robust active shape model search. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 517–530. Springer, Heidelberg (2002)
Wang, Y., Staib, L.: Statistical shape and smoothness models for boundary finding with correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(7), 738–743 (2000)
Subasic, M., Loncaric, S., Sorantin, E.: 3D image analysis of abdominal aortic aneurysm. In: Sonka, M., Fitzpatrick, M. (eds.) Medical Imaging: Image Processing, Proceedings of SPIE, vol. 4684, pp. 1681–1689. SPIE Press, Bellingham (2002)
Bosch, H., Mitchell, S., Lelieveldt, B., Nijland, F., Kamp, O., Sonka, M., Reiber, J.: Active appearance-motion models for endocardial contour detection in time sequences of echocardiograms. In: Sonka, M., Hanson, K. (eds.) Medical Imaging: Image Processing, Proceedings of SPIE, vol. 4322, SPIE Press, Bellingham (2001)
Brejl, M., Sonka, M.: Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples. IEEE Transactions on Medical Imaging 19(10), 973–985 (2000)
Wever, J., Blankensteijn, J., van Rijn, J., Broeders, I., Eikelboom, B., Mali, W.: Interand intra-observer variability of CTA measurements obtained after endovascular repair of abdominal aortic aneurysms. American Journal of Roentgenology 175(5), 1282–1297 (2000)
Bland, J., Altman, D.: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (1), 307–310 (1986)
Jain, A., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Bruijne, M., van Ginneken, B., Viergever, M.A., Niessen, W.J. (2003). Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-45087-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40560-3
Online ISBN: 978-3-540-45087-0
eBook Packages: Springer Book Archive