Abstract
Fusion of images with same or different modalities has been conquering medical imaging field more rapidly due to the presence of highly accessible patients’ information in recent years. For example, cross platform non-rigid registration of CT with MRI images has found a significant role in different clinical application. In some instances labelling of anatomical features by medical experts are also involved to further improve the accuracy and authenticity of the registration. Being motivated by these, we propose a new algorithm to compute diffeomorphic hybrid multi-modality registration with large deformations. Our iterative scheme consists of mainly two steps. First, we obtain the optimal Beltrami coefficient corresponding to the diffeomorphic mapping that exactly superimposes the feature points. The second step detects the intensity difference in the framework of mutual information. A non-rigid deformation which minimizes the intensity difference is then obtained. Experiments have been carried out on both synthetic and real data. Results demonstrate the stability and efficacy of the proposed algorithm to obtain diffeomorphic image registration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Heckbert, P.S.: Survey of texture mapping. IEEE Comput. Graphics Appl. 6, 56–67 (1986)
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32, 1153–1190 (2013)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003)
Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989)
Joshi, S.C., Miller, M.I.: Landmark matching via large deformation diffeomorphisms. IEEE Trans. Image Process. 9, 1357–1370 (2000)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45, S61–S72 (2009)
Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2, 243–260 (1998)
Glocker, B., Sotiras, A., Komodakis, N., Paragios, N.: Deformable medical image registration: setting the state of the art with discrete methods. Annu. Rev. Biomed. Eng. 13, 219–244 (2011)
Christensen, G.E., Johnson, H.J.: Consistent image registration. IEEE Trans. Med. Imaging 20, 568–582 (2001)
Chanwimaluang, T., Fan, G., Fransen, S.R.: Hybrid retinal image registration. IEEE Trans. Inf. Technol. Biomed. 10, 129–142 (2006)
James, A., Dasarathy, B.: Medical image fusion: a survey of the state of the art. Inf. Fusion 19, 4–19 (2014)
Li, H., Manjunath, B., Mitra, S.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57, 235–245 (1995)
Naidu, V., Raol, J.: Pixel-level image fusion using wavelets and principal component analysis. Def. Sci. J. 58, 338–352 (2008)
Lam, K.C., Lui, L.M.: Landmark and intensity based registration with large deformations via quasi-conformal maps. SIAM J. Imaging Sci. 7, 2364–2392 (2014)
Gardiner, F.P., Lakic, N.: Quasiconformal TeichmĂĽller Theory. Mathematical Surveys and Monographs. American Mathematical Society, Providence (2000)
Kroon, D.: Multimodality non-rigid demon algorithm image registration. Robust Non-rigid Point Matching 14, 120–126 (2008)
Astala, K., Iwaniec, T., Martin, G.: Elliptic Partial Differential Equations and Quasiconformal Mappings in the Plane. Oxford Graduate Texts in Mathematics. Princeton University Press, Princeton (2008)
Lui, L.M., Lam, K.C., Wong, T.W., Gu, X.F.: Texture map and video compression using Beltrami representation. SIAM J. Imaging Sci. 6, 1880–1902 (2013)
Barzilai, J., Borwein, J.: Two-point step size gradient methods. IMA J. Numer. Anal. 8, 141–148 (1988)
Acknowledgements
This project is supported by HKRGC GRF (Project ID: 2130363 Reference: 402413)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lam, K.C., Lui, L.M. (2015). Quasi-Conformal Hybrid Multi-modality Image Registration and its Application to Medical Image Fusion. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-27857-5_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27856-8
Online ISBN: 978-3-319-27857-5
eBook Packages: Computer ScienceComputer Science (R0)