Abstract
In this paper, we present the application of canonical correlation analysis to investigate how the shapes of different structures within the brain vary statistically relative to each other. Canonical correlation analysis is a multivariate statistical technique which extracts and quantifies correlated behaviour between two sets of vector variables. Firstly, we perform non-rigid image registration of 93 sets of 3D MR images to build sets of surfaces and correspondences for sub-cortical structures in the brain. Canonical correlation analysis is then used to extract and quantify correlated behaviour in the shapes of each pair of surfaces. The results show that correlations are strongest between neighbouring structures and reveal symmetry in the correlation strengths for the left and right sides of the brain.
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Ashburner, J., Friston, K.J.: Voxel-based morphometry – the methods. NeuroImage 11(6), 805–821 (2000)
Ashburner, J., Friston, K.J.: Why voxel-based morphometry should be used. NeuroImage 14(6), 1238–1243 (2001)
Ashburner, J., Hutton, C., Frackowiak, R., Johnsrude, I., Price, C., Friston, K.: Identifying global anatomical differences: Deformation-based morphometry. Human Brain Mapping 6, 638–657 (1998)
Bajcsy, R., Kovačič, S.: Multiresolution elastic matching. Computer Vision, Graphics and Image Processing 46, 1–21 (1989)
Bookstein, F.L.: Voxel-based morphometry should not be used with imperfectly registered images. NeuroImage 14(6), 1452–1462 (2001)
Bro-Nielsen, M., Gramkow, C.: Fast fluid registration of medical images. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 267–276. Springer, Heidelberg (1996)
Christensen, G.E., Joshi, S.C., Miller, M.I.: Individualizing anatomical atlases of the head. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 434–348. Springer, Heidelberg (1996)
Christensen, G.E., Miller, M.I., Mars, J.L., Vannier, M.W.: Automatic analysis of medical images using a deformable textbook. In: Computer Assisted Radiology, Berlin, Germany, pp. 146–151. Springer, Heidelberg (1995)
Chung, M.K., Worsley, K.J., Paus, T., Collins, D.L., Cherif, C., Giedd, J.N., Rapoport, J.L., Evans, A.C.: A unified statistical approach to deformation-based morphometry. NeuroImage 14(3), 595–606 (2001)
Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography 18(2), 192–205 (1994)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 484–498. Springer, Heidelberg (1998)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Gee, J., Reivich, M., Bajcsy, R.: Elastically deforming 3D atlas to match anatomical brain images. Journal of Computer Assisted Tomography 17(2), 225–236 (1993)
Grenander, U., Miller, M.I.: Computational anatomy: An emerging discipline. Quarterly of Applied Mathematics 56(4), 617–694 (1998)
Horn, B.: Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America 4, 629–642 (1987)
Laudadio, T., Pels, P., Lathauwer, L., Hecke, P., Huffel, S.: Tissue segmentation and classification of mrsi data using canonical correlation analysis. Magnetic Resonance in Medicine 54, 1519–1529 (2005)
Liu, T., Shen, D., Davatzikos, C.: Predictive modeling of anatomic structures using canonical correlation analysis. In: IEEE International Symposium on Biomedical Imaging (2004)
Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate analysis. Academic Press, Belfast (1982)
Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J.: A probabilistic atlas of the human brain: Theory and rationale for its developement. The international consortium for brain mapping. NeuroImage 2(2), 89–101 (1995)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)
Zollei, L., Panych, L., Grimson, E., Wells, W.: Exploratory identification of cardiac noise in fmri images. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 475–482. Springer, Heidelberg (2003)
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Rao, A., Babalola, K., Rueckert, D. (2006). Canonical Correlation Analysis of Sub-cortical Brain Structures Using Non-rigid Registration. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds) Biomedical Image Registration. WBIR 2006. Lecture Notes in Computer Science, vol 4057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784012_9
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DOI: https://doi.org/10.1007/11784012_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35648-6
Online ISBN: 978-3-540-35649-3
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