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MICCAI
2009
Springer

Cortical Shape Analysis in the Laplace-Beltrami Feature Space

10 years 11 months ago
Cortical Shape Analysis in the Laplace-Beltrami Feature Space
For the automated analysis of cortical morphometry, it is critical to develop robust descriptions of the position of anatomical structures on the convoluted cortex. Using the eigenfunction of the LaplaceBeltrami operator, we propose in this paper a novel feature space to characterize the cortical geometry. Derived from intrinsic geometry, this feature space is invariant to scale and pose variations, anatomically meaningful, and robust across population. A learning-based sulci detection algorithm is developed in this feature space to demonstrate its application in cortical shape analysis. Automated sulci detection results with 10 training and 15 testing surfaces are presented .
Yonggang Shi, Ivo D. Dinov, Arthur W. Toga
Added 06 Nov 2009
Updated 15 Nov 2009
Type Conference
Year 2009
Where MICCAI
Authors Yonggang Shi, Ivo D. Dinov, Arthur W. Toga
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