Abstract
Image registration is a fundamental task in image processing. Its aim is to match two or more pictures taken with the same or from different sensors, at different times or from different viewpoints. In image registration the use of an adequate measure of alignment is a crucial issue. Current techniques are classified in two broad categories: pixel based and feature based. All methods include some similarity measure. In this paper a new measure that combines mutual information ideas, spatial information and feature characteristics, is proposed. Edge points obtained from a Canny edge detector are used as features. Feature characteristics like location, edge strength and orientation, are taken into account to compute a joint probability distribution of corresponding edge points in two images. Mutual information based on this function is maximized to find the best alignment parameters. The approach has been tested with a collection of medical images (Nuclear Magnetic Resonance and radiotherapy portal images) and conventional video sequences, obtaining encouraging results.
Keywords
- Mutual Information
- Video Sequence
- Joint Probability
- Image Registration
- Edge Point
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.
Chapter PDF
References
Pluim, J.W., Maintz, J.B.A., Viergever, M.A.: Mutual information based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22, 986–1004 (2003)
Viola, P., Wells, W.M.: Alignment by maximization of mutual information. International Journal on Computer Vision 24, 137–154 (1997)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16, 187–198 (1997)
Tomazevic, D., Likar, B., Pernus, F.: Multi-feature mutual information. In: Sonka, M. (ed.) Medical Imaging: Image Processing, vol. 5070, pp. 234–242. SPIE Press (2004)
Lester, H., Arriage, S.R.: A survey of hierarchical non-linear medical image registration. Pattern Recognition 32, 129–149 (1999)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)
Maintz, J., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1999)
Leszczynski, K., Loose, S., Dunscombe, P.: Segmented chamfer matching for the registration of field borders in radiotherapy images. Physics Medicine and Bilogy 40, 83–94 (1995)
Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 849–865 (1988)
Hristov, D.H., Fallone, B.G.: A gray-level image alignment algorithm for registration of portal images and digitally reconstructed radiographs. Medical Physics 23, 75–84 (1996)
Langmack, K.A.: Portal imaging. The British Journal of Radiology 74, 789–804 (2001)
Kim, J., Fessler, J.A., Lam, K.L., Balter, J.M., Ten-Haken, R.K.: A feasibility study of mutual information based setup error estimation for radiotherapy. Medical Physics 28, 2507–2517 (2001)
Rangarajan, A., Chui, H., Duncan, J.: Rigid point feature registration using mutual information. Medical Image Analysis 3, 425–440 (1999)
Pluim, J., Maintz, J.B., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE Transactions on Medical Imaging 19, 809–814 (2000)
West, J., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography 21, 554–566 (1997)
Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Physcis in Medicine and Biology 46, R1–R45 (2001)
Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S.: Integrated intensity and point-feature nonrigid registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 763–770. Springer, Heidelberg (2004)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Maes, F., Vandermeulen, D., Suetens, P.: Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Medical Image Analysis 3, 373–386 (1999)
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
Alvarez, N.A., Sanchiz, J.M. (2005). Image Registration from Mutual Information of Edge Correspondences. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_55
Download citation
DOI: https://doi.org/10.1007/11578079_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
Publish with us
-
Published in cooperation with
http://www.iapr.org/
