@CONFERENCE\{IMM2007-04971,
    author       = "H. Olafsd\'{o}ttir and M. S. Hansen and K. Sj{\"{o}}strand and T. A. Darvann and N. V. Hermann and E. Oubel and B. K. Ersb{\o}ll and R. Larsen and A. P. Larsen and C. A. Perlynn and G. M. Morriss-Kay and S. Kreiborg",
    title        = "Sparse Statistical Deformation Model for the Analysis of Craniofacial Malformations in the Crouzon Mouse",
    year         = "2007",
    keywords     = "Sparse {PCA,} statistical deformation model, Crouzon syndrome",
    booktitle    = "Scandinavian Conference on Image Analysis 2007",
    volume       = "",
    series       = "Lecture notes in computer science (LNCS)",
    editor       = "Bjarne Kj{\ae}r Ersb{\o}ll, Ingela Nystr{\"{o}}m, Ivar Austvoll, Janne Heikkil{\"{a}}",
    publisher    = "Springer",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4971-full.html",
    abstract     = "Crouzon syndrome is characterised by the premature fusion
of cranial sutures. Recently the rst genetic Crouzon mouse model was
generated. In this study, Micro {CT} skull scannings of wild-type mice and
Crouzon mice were investigated. Using nonrigid registration, a wild-type
mouse atlas was built. The atlas was registered to all mice providing
parameters controlling the deformations for each subject. Our previous
 {PCA-}based statistical deformation model on these parameters revealed
only one discriminating mode of variation. Aiming at distributing the
discriminating variation over more modes we built a dierent model using
Independent Component Analysis (ICA). Here, we focus on a third
method, sparse {PCA} (SPCA), which aims at approximating the properties
of a standard {PCA} while introducing sparse modes of variation.
This approach is compared to a standard {PCA} and {ICA}. The results
show that the {SPCA} outperforms both {ICA} and {PCA} with respect to
the Fisher discriminant."
}