English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Sparse shape representation using the Laplace-Beltrami eigenfunctions and its application to modeling subcortical structures

MPS-Authors
There are no MPG-Authors available
Locator
There are no locators available
Fulltext (public)

Kim_2012_MMBIA.pdf
(Preprint), 3MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Kim, S.-G., Chung, M. K., Schaefer, S. M., van Reekum, C., & Davidson, R. J. (2012). Sparse shape representation using the Laplace-Beltrami eigenfunctions and its application to modeling subcortical structures. In Proceedings of the 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) (pp. 25-32).


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-7396-0
Abstract
We present a new sparse shape modeling framework on the Laplace-Beltrami (LB) eigenfunctions. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes by forming a Fourier series expansion. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we propose to filter out only the significant eigenfunctions by imposing l1-penalty. The new sparse framework can further avoid additional surface-based smoothing often used in the field. The proposed approach is applied in investigating the influence of age (38-79 years) and gender on amygdala and hippocampus shapes in the normal population. In addition, we show how the emotional response is related to the anatomy of the subcortical structures.