Abstract
This paper presents an approach to fast image registration through probabilistic pixel sampling. We propose a practical scheme to leverage the benefits of two state-of-the-art pixel sampling approaches: gradient magnitude based pixel sampling and uniformly random sampling. Our framework involves learning the optimal balance between the two sampling schemes off-line during training, based on a small training dataset, using particle swarm optimization. We then test the proposed sampling approach on 3D rigid registration against two state-of-the-art approaches based on the popular, publicly available, Vanderbilt RIRE dataset. Our results indicate that the proposed sampling approach yields much faster, accurate and robust registration results when compared against the state-of-the-art.
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
Benhimane, S., Ladikos, A., Lepetit, V., Navab, N.: Linear and quadratic subsets for template-based tracking. In: Proc. IEEE CVPR, Minneapolis, MN, USA (June 2007)
Brooks, R., Arbel, T.: The importance of scale when selecting pixels for image registration. In: Proc. CRV, Paris, France, pp. 235–242 (May 2007)
Brooks, R.: Efficient and Reliable Methods for Direct Parameterized Image Registration. Ph.D. thesis, McGill University (2008)
Dellaert, F., Collins, R.: Fast image-based tracking by selective pixel integration. In: Proc. ICCV, Kerkyra, Greece (September 1999)
Fitzpatrick, J., West, J., Maurer, C.: Predicting error in rigid-body point-based registration. IEEE TMI 17(5), 694–702 (1998)
Freiman, M., Werman, M., Joskowicz, L.: A curvelet-based patient-specific prior for accurate multi-modal brain image rigid registration. Med. Imag. Anal. 15(1), 125–132 (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942–1948 (November 1995)
Lu, X., Zhang, S., Su, H., Chen, Y.: Mutual information-based multimodal image registration using a novel joint histogram estimation. Computerized Medical Imaging and Graphics 32(3), 202–209 (2008)
Mattes, D., Haynor, D., Vesselle, H., Lewellen, T., Eubank, W.: PET-CT image registration in the chest using free-form deformations. IEEE TMI 22(1), 120–128 (2003)
Reeves, S.J., Hezar, R.: Selection of observations in magnetic resonance spectroscopic imaging. In: Proc. ICIP, vol. 1, pp. 641–644 (October 1995)
Sabuncu, M.R., Ramadge, P.J.: Gradient based nonuniform subsampling for information-theoretic alignment methods. In: Proc. IEEE IEMBS., vol. 1, pp. 1683–1686 (September 2004)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)
Viola, P., Wells, W.M.: Alignment by maximization of mutual information. IJCV 24(2), 137–154 (1997)
Yushkevich, P.A., Piven, J., Hazlett, C.H., Smith, G.R., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (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
Oreshkin, B.N., Arbel, T. (2012). Optimization over Random and Gradient Probabilistic Pixel Sampling for Fast, Robust Multi-resolution Image Registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-31340-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31339-4
Online ISBN: 978-3-642-31340-0
eBook Packages: Computer ScienceComputer Science (R0)