| Abstract | Crouzon syndrome is characterised by the premature fusion of cranial sutures. Recently the first 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
craniofacial 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 different
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.
The results show that SPCA outperforms both ICA and PCA with
respect to the Fisher discriminant, although many similarities are found
with respect to ICA.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
craniofacial 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 di erent
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.
The results show that SPCA outperforms both ICA and PCA with
respect to the Fisher discriminant, although many similarities are found
with respect to ICA. |