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Proceedings Paper

Smoothing fields of frames using conjugate norms on reproducing kernel Hilbert spaces
Author(s): Hsiao-Fang Chou; Laurent Younes
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Paper Abstract

Diffusion tensor imaging provides structural information in medical images in the form of a symmetric positive matrix that provides, at each point, the covariance of water diffusion in the tissue. We here describe a new approach designed for smoothing this tensor by directly acting on the field of frames provided by the eigenvectors of this matrix. Using a representation of fields of frames as linear forms acting on smooth tensor fields, we use the theory of reproducing kernel Hilbert spaces to design a measure of smoothness based on kernels which is then used in a denoising algorithm. We illustrate this with brain images and show the impact of the procedure on the output of fiber tracking in white matter.

Paper Details

Date Published: 2 February 2009
PDF: 9 pages
Proc. SPIE 7246, Computational Imaging VII, 724607 (2 February 2009); doi: 10.1117/12.815280
Show Author Affiliations
Hsiao-Fang Chou, Johns Hopkins Univ. (United States)
Laurent Younes, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 7246:
Computational Imaging VII
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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