Abstract
Automatic delineation of anatomical structures in 3-D volumetric data is a challenging task due to the complexity of the object appearance as well as the quantity of information to be processed. This makes it increasingly difficult to encode prior knowledge about the object segmentation in a traditional formulation as a perceptual grouping task. We introduce a fast shape segmentation method for 3-D volumetric data by extending the 2-D database-guided segmentation paradigm which directly exploits expert annotations of the interest object in large medical databases. Rather than dealing with 3-D data directly, we take advantage of the observation that the information about position and appearance of a 3-D shape can be characterized by a set of 2-D slices. Cutting these multiple slices simultaneously from the 3-D shape allows us to represent and process 3-D data as efficiently as 2-D images while keeping most of the information about the 3-D shape. To cut slices consistently for all shapes, an iterative 3-D non-rigid shape alignment method is also proposed for building local coordinates for each shape. Features from all the slices are jointly used to learn to discriminate between the object appearance and background and to learn the association between appearance and shape. The resulting procedure is able to perform shape segmentation in only a few seconds. Extensive experiments on cardiac ultrasound images demonstrate the algorithm’s accuracy and robustness in the presence of large amounts of noise.
Chapter PDF
Similar content being viewed by others
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.
References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 2, 321–331 (1988)
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)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Comanicu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24, 603–619 (2002)
Mitchell, S., Bosch, J., Lelieveldt, B., van der Geest, R., Reiber, J., Sonka, M.: 3-d active appearance models: segmentation of cardiac mr and ultrasound images. IEEE Transactions on Medical Imaging 21, 1167–1178 (2002)
Stegmann, M.B.: Generative Interpretation of Medical Images. PhD thesis, Technical University of Denmark (2004)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37 (1995)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (2001)
Georgescu, B., Zhou, X., Comaniciu, D., Gupta, A.: Database-guided segmentation of anatomical structures with complex appearance. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (2005)
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vision 38, 15–33 (2000)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley Interscience, Hoboken (1973)
Nadaraya, E.A.: On estimating regression. Theory Prob. Appl. 10, 186–190 (1964)
Watson, G.S.: Smooth regression analysis. Sankhy a, Series A 26, 359–372 (1964)
Epanechnikov, V.: Nonparametric estimates of a multivariate probability density. Theory of Probability and its Applications 14, 153–158 (1969)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hong, W., Georgescu, B., Zhou, X.S., Krishnan, S., Ma, Y., Comaniciu, D. (2006). Database-Guided Simultaneous Multi-slice 3D Segmentation for Volumetric Data. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744085_31
Download citation
DOI: https://doi.org/10.1007/11744085_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33838-3
Online ISBN: 978-3-540-33839-0
eBook Packages: Computer ScienceComputer Science (R0)