TY - GEN
T1 - Sparse statistical deformation model for the analysis of craniofacial malformations in the crouzon mouse
AU - Ólafsdóttir, Hildur
AU - Hansen, Michael Sass
AU - Sjöstrand, Karl
AU - Darvann, Tron A.
AU - Hermann, Nuno V.
AU - Oubel, Estanislao
AU - Ersbøll, Bjarne K.
AU - Larsen, Rasmus
AU - Frangi, Alejandro F.
AU - Larsen, Per
AU - Perlyn, Chad A.
AU - Morriss-Kay, Gillian M.
AU - Kreiborg, Sven
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38049017021&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-73040-8_12
DO - 10.1007/978-3-540-73040-8_12
M3 - Conference contribution
AN - SCOPUS:38049017021
SN - 9783540730392
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 112
EP - 121
BT - Image Analysis - 15th Scandinavian Conference, SCIA 2007, Proceedings
PB - Springer-Verlag Italia
T2 - 15th Scandinavian Conference on Image Analysis, SCIA 2007
Y2 - 10 June 2007 through 14 June 2007
ER -