ausblenden:
Schlagwörter:
Eigenvalues and eigenfunctions;Electromyography;Hippocampus;Noise;Shape;Solid modeling;Vectors;Fourier series;biomedical MRI;brain;eigenvalues and eigenfunctions;filtering theory;image denoising;image reconstruction;image representation;medical image processing;surface reconstruction;Fourier series expansion;LB-eigenfunctions;Laplace-Beltrami eigenfunction;amygdala shape;emotional response;high frequency noise;hippocampus shape;l1-penalty;sparse shape modeling framework;sparse shape representation;subcortical structure;surface reconstruction;surface-based smoothing;
Zusammenfassung:
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.
