Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex


Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based… (More)
DOI: 10.1007/978-3-319-59050-9_3