Abstract
Although numerous methods to register brains of different individuals have been proposed, few work has been done to evaluate the performances of different registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the quality of the registration. Experiments have been conducted for 5 methods, through a database of 18 subjects. We focused more extensively on the registration of cortical landmarks that have a particular relevance in the context of anatomical-functional normalization. For global measures, results show that the quality of the registration is directly related to the transformation’s degrees of freedom. However, local measures based on the matching of cortical sulci, did not make it possible to show significant differences between affine and non linear methods.
Chapter PDF
Similar content being viewed by others
References
JH. Van Bemmel, MA. Musen. Handbook of medical informatics. Springer, 1997.
L. Collins, A. Evans. Animal: validation and applications of nonlinear registration-based segmentation. IJPRAI, 8(11):1271–1294, 1997.
T. Cootes, C. Taylor, D. Hooper, J. Graham. Active shape models-their training and application. CVIU, 61(1):31–59, 1995.
A. Evans, L. Collins, B. Milner. A MRI-based stereotaxic atlas from 250 young normal subjects. Soc. Neuroscience abstract, 18:408, 1992.
L. Florack, B. Romeny, J. Koenderink, M. Viergever. Scale and the differential structure of images. IVC, 10:376–388, 1992.
G. Le Goualher, C. Barillot, and Y. Bizais. Modeling cortical sulci with active ribbons. IJPRAI, 8(11):1295–1315, 1997.
P. Hellier, C. Barillot, E. Mémin, and P. Pérez. Hierarchical estimation of a dense deformation field for 3D robust registration. In IEEE TMI, 20(5):388–402, 2001.
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Suetens. Multimodality image registration by maximisation of mutual information. IEEE TMI, 16(2):187–198, 1997.
J. Maintz, MA. Viergever.–A survey of medical image registration. Medical Image Analysis, 2(1):1–36, 1998.
J. Mazziotta, A. Toga, A. Evans, P. Fox, and J. Lancaster. A probabilistic atlas of the human brain: theory and rationale for its development. Neuroimage, 2:89–101, 1995.
M. Ono, S. Kubik, C. Abernathey.-Atlas of the cerebral sulci.-Verlag, 1990.
J. Talairach, P. Tournoux. Co-planar stereotaxic atlas of the human brain. Georg Thieme Verlag, Stuttgart, 1988.
JP. Thirion. Image matching as a diffusion process: an analogy with Maxwell’s demons. Medical Image Analysis, 2(3):243–260, 1998.
P. Thompson, R. Woods, M. Mega, A. Toga. Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain. HBM, 9:81–92, 2000.
P. Viola, W. Wells. Alignment by maximisation of mutual information. IJCV, 24(2):137–154, 1997.
J. West, J. Fitzpatrick, et al. Comparaison and evaluation of retrospective intermodality brain image registration techniques. JCAT, 21(4):554–566, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hellier, P. et al. (2001). Retrospective Evaluation of Inter-subject Brain Registration. In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3_31
Download citation
DOI: https://doi.org/10.1007/3-540-45468-3_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42697-4
Online ISBN: 978-3-540-45468-7
eBook Packages: Springer Book Archive