TY - GEN
T1 - Morphological appearance manifolds in computational anatomy
T2 - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
AU - Davatzikos, Christos
AU - Lian, Naixiang
PY - 2009
Y1 - 2009
N2 - We present an extension of the conventional computational anatomy framework to account for confounding variations due to selection of parameters and templates, by learning the equivalence class derived from the multitude of representations of an individual anatomy. A morphological appearance manifold obtained by varying parameters of the template warping procedure is estimated. Group-wise registration and statistical analysis is then based on a constrained optimization framework, which employs a minimum variance criterion to perform manifold walking, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations that reflect purely underlying biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local linear approximations of the manifold via PCA.
AB - We present an extension of the conventional computational anatomy framework to account for confounding variations due to selection of parameters and templates, by learning the equivalence class derived from the multitude of representations of an individual anatomy. A morphological appearance manifold obtained by varying parameters of the template warping procedure is estimated. Group-wise registration and statistical analysis is then based on a constrained optimization framework, which employs a minimum variance criterion to perform manifold walking, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations that reflect purely underlying biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local linear approximations of the manifold via PCA.
UR - http://www.scopus.com/inward/record.url?scp=70449341663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449341663&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2009.5193179
DO - 10.1109/ISBI.2009.5193179
M3 - Conference contribution
AN - SCOPUS:70449341663
SN - 9781424439324
T3 - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
SP - 826
BT - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging
Y2 - 28 June 2009 through 1 July 2009
ER -