Cortical Shape Analysis in the Laplace-Beltrami Feature Space

Authors:
Yonggang Shi;Ivo Dinov;Arthur W. Toga
Affiliations:
Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA;Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA;Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

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 Laplace-Beltrami 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.