Abstract
We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently implemented on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast – registration of two cortical mesh models with more than 100k nodes takes less than 5 minutes, comparable to the fastest surface registration algorithms. Moreover, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different settings: (1) parcellation in a set of in-vivo cortical surfaces and (2) Brodmann area localization in ex-vivo cortical surfaces.
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
Arsigny, V., et al.: A Log-Euclidean Framework for Statistics on Diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)
Ashburner, J., Andersson, J., Friston, K.: High-dimensional Image Registration using Symmetric Priors. NeuroImage 9, 619–628 (1999)
Ashburner, J.: A Fast Diffeomorphic Image Registration Algorithm. NeuroImage 38, 95–113 (2007)
Beg, M., et al.: Computing Large Deformation Metric Mapping via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)
Benjamini, Y., Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Mult. Testing. J. Roy. Stats. Soc. 57(1), 289–300 (1995)
Cachier, P.: Iconic Feature Based Non-rigid Registration: The PASHA Algorithm. CVIU 89(2-3), 272–298 (2003)
Bro-Nielsen, M., Gramkow, C.: Fast Fluid Registration of Medical Images Visualization in Biomedical Computing, 267–276 (1996)
Desikan, R., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage (2006)
Durrleman, S., et al.: Measuring Brain Variability via Sulcal Lines Registration: a Diffeomorphic Approach. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 675–682. Springer, Heidelberg (2007)
Eckstein, I., et al.: Generalized Surface Flows for Deformable Registration and Cortical Matching. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 692–700. Springer, Heidelberg (2007)
Fischl, B., et al.: High-resolution intersubject averaging and a coordinate system for the cortical surface. HBM 8(4), 272–284 (1999)
Fischl, B., et al.: Automatically Parcellating the Human cerebral Cortex. Cerebral Cortex 14, 11–22 (2004)
Fischl, B., et al.: Cortical Folding Patterns and Predicting Cytoarchictecture. Cerebral Cortex (2007)
Geng, X., et al.: Transitive Inverse-Consistent Manifold Registration. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 468–479. Springer, Heidelberg (2005)
Glaunès, J., et al.: Landmark Matching via Large Deformation Diffeomorphisms on the Sphere. Journal of Mathematical Imaging and Vision 20, 179–200 (2004)
Nielsen, M., et al.: Brownian Warps: A Least Committed Prior for Non-rigid Registration. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 557–564. Springer, Heidelberg (2002)
Schleicher, A., et al.: Observer independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics. NeuroImage 9, 165–177 (1999)
Tosun, D., Prince, J.: Cortical Surface Alignment Using Geometry Driven Multispectral Optical Flow. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 480–492. Springer, Heidelberg (2005)
Thirion, J.: Image Matching as a Diffusion Process: an Analogy with Maxwell’s Demons. Medical Image Analysis 2(3), 243–260 (1998)
Thompson, P., et al.: Mathematical/Computational Challenges in Creating Deformable and Probabilistic Atlases of the Human Brain. HBM 9(2), 81–92 (2000)
Van Essen, D., et al.: Functional and structural mapping of human cerebral cortex: solutions are in the surfaces. PNAS 95(3), 788–795 (1996)
Vercauteren, T., et al.: Non-parameteric Diffeomorphic Image Registration with the Demons Algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)
Yeo, B.T.T., et al.: Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 683–691. Springer, Heidelberg (2007)
Yeo, B.T.T., et al.: What Data to Co-register for Computing Atlases. In: MMBIA, Proc. ICCV, pp. 1–8 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yeo, B.T.T., Sabuncu, M., Vercauteren, T., Ayache, N., Fischl, B., Golland, P. (2008). Spherical Demons: Fast Surface Registration. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_89
Download citation
DOI: https://doi.org/10.1007/978-3-540-85988-8_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85987-1
Online ISBN: 978-3-540-85988-8
eBook Packages: Computer ScienceComputer Science (R0)