Sparse Shape Representation using the Laplace-Beltrami Eigenfunctions and Its Application to Modeling Subcortical Structures

By Seung-Goo Kim, Moo K. Chung, Stacey M. Schaefer, C. V. van Reekum, Richard J. Davidson. 2012

8 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, methodology): “Good examples of using the LASSO in imaging are fMRI analysis presented in Bunea et al. (2011) and shape analysis presented in Kim et al. (2012); Chung (2012).”