Abstract
Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21, 1421–1439 (2002)
Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vis. 24, 137–154 (1997)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999)
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61, 139–157 (2005)
Paquin, D., Levy, D., Schreibmann, E., Xing, L.: Multiscale image registration. Math. Biosci. Eng. 3, 389–418 (2006)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008)
Maes, F., Vandermeulen, D., Suetens, P.: Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Med. Image Anal. 3, 373–386 (1999)
Jia, H., Yap, P.-T., Shen, D.: Iterative multi-atlas-based multi-image segmentation with treebased registration. Neuroimage 59, 422–430 (2012)
Wang, Q., Kim, M., Shi, Y., Wu, G., Shen, D.: Predict brain MR image registration via sparse learning of appearance and transformation. Med. Image Anal. 20, 61–75 (2015)
Aljabar, P., Wolz, R., Rueckert, D.: Manifold learning for medical image registration, segmentation, and classification. In: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. IGI Global (2012)
Ye, D.H., Hamm, J., Kwon, D., Davatzikos, C., Pohl, K.M.: Regional manifold learning for deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 131–138. Springer, Heidelberg (2012)
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62, 782–790 (2012)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45, S61–S72 (2009)
Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 786–802 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, J., Wang, Q., Wu, G., Shen, D. (2016). Cross-Manifold Guidance in Deformable Registration of Brain MR Images. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-43775-0_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43774-3
Online ISBN: 978-3-319-43775-0
eBook Packages: Computer ScienceComputer Science (R0)