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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Suyash P. Awate, Richard M. Leahy, Anand A. Joshi

Erschienen in: Information Processing in Medical Imaging

Verlag: Springer International Publishing

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Abstract

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 descriptor lies on a Riemannian manifold. We propose new regularized nonlinear methods for (i) detecting group differences, using a Mercer kernel with an implicit lifting map to a reproducing kernel Hilbert space, and (ii) regression against clinical variables, using kernel density estimation. For both methods, we employ kernels that exploit the Riemannian structure. Results on simulated and clinical data shows the improved accuracy and stability of our approach in cortical-sheet analysis.

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Vorheriges Kapitel Inconsistency of Template Estimation with the Fréchet Mean in Quotient Space

Nächstes Kapitel Conditional Local Distance Correlation for Manifold-Valued Data

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Titel: Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex
verfasst von: Suyash P. Awate
Richard M. Leahy
Anand A. Joshi
Verlag: Springer International Publishing
Buch: Information Processing in Medical Imaging
Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59050-9_3

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