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

Kim, S.-G., Chung, M. K., Schaefer, S. M., Van Reekum, C. and Davidson, R. J. (2012) Sparse shape representation using the Laplace-Beltrami eigenfunctions and its application to modeling subcortical structures. In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), 9-10 Jan 2012.

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...

Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Centre for Integrative Neuroscience and Neurodynamics (CINN)
Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology
Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Psychopathology and Affective Neuroscience
ID Code:30941

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