Docteur de l'université Automatic Segmentation and Shape Analysis ...

166 BIBLIOGRAPHY
Kendall, D. G. (1977). The diffusion of shape. Advances in Applied Probability,
9(3):428–430. 2.2
Kendall, D. G. (1984). Shape manifolds, procrustean metrics, and complex pro-
jective spaces. Bulletin of the London Mathematical Society, 16(2):81 –121. 2.2,
5.2
Kendall, D. G. (1989). A survey of the statistical theory of shape. Statistical
Science, 4(2):87–99. 2.2
Kendall, D. G., Barden, D., Carne, T. K., and Le, H. (1999). Shape and shape
theory. Wiley, New York. 2.2, 2.2
Khan, A. R., Cherbuin, N., Wen, W., Anstey, K. J., Sachdev, P., and Beg, M. F.
(2011). Optimal weights for local multi-atlas fusion using supervised learning
and dynamic information (SuperDyn): validation on hippocampus segmenta-
tion. NeuroImage, 56(1):126–139. 2.1.2.3
Khan, A. R., Wang, L., and Beg, M. F. (2008). FreeSurfer-initiated fully-
automated subcortical brain segmentation in MRI using large deformation dif-
feomorphic metric mapping. NeuroImage, 41(3):735–746. 2.1.2.3
Klemencic, J., Pluim, J., Viergever, M., Schnack, H., and Valencic, V. (2004).
Non-rigid registration based active appearance models for 3D medical image seg-
mentation. Journal of Imaging Science and Technology, 48(2):166–171. 2.1.2.3
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer,
J. D., Fox, N. C., Jack, C. R., Ashburner, J., and Frackowiak, R. S. J.
(2008). Automatic classification of MR scans in Alzheimer’s disease. Brain,
131(3):681–689. 5.3
Kolmogorov, V. and Zabin, R. (2004). What energy functions can be minimized via
graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence,
26(2):147–159. 2.1.2.3