FLASH: Fast Landmark Aligned Spherical Harmonic Parameterization for Genus-0 Closed Brain Surfaces
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AbstractSurface registration between cortical surfaces is crucial in medical imaging for performing systematic comparisons between brains. Landmark-matching registration that matches anatomical features, called the sulcal landmarks, is often required to obtain a meaningful 1-1 correspondence between brain surfaces. This is commonly done by parameterizing the surface onto a simple parameter domain, such as the unit sphere, in which the sulcal landmarks are consistently aligned. Landmark-matching surface registration can then be obtained from the landmark aligned parameterizations. For genus-0 closed brain surfaces, the optimized spherical harmonic parameterization, which aligns landmarks to consistent locations on the sphere, has been widely used. This approach is limited by the loss of bijectivity under large deformations and the slow computation. In this paper, we propose FLASH, a fast algorithm to compute the optimized spherical harmonic parameterization with consistent landmark alignment. This is achieved by formulating the optimization problem to (C) over bar and thereby linearizing the problem. Errors introduced near the pole are corrected using quasi-conformal theories. Also, by adjusting the Beltrami differential of the mapping, a diffeomorphic (1-1, onto) spherical parameterization can be effectively obtained. The proposed algorithm has been tested on 38 human brain surfaces. Experimental results demonstrate that the computation of the landmark aligned spherical harmonic parameterization is significantly accelerated using the proposed algorithm.
All Author(s) ListChoi PT, Lam KC, Lui LM
Journal nameSIAM Journal on Imaging Sciences
Year2015
Month1
Day1
Volume Number8
Issue Number1
PublisherSIAM PUBLICATIONS
Pages67 - 94
ISSN1936-4954
LanguagesEnglish-United Kingdom
KeywordsBeltrami differential; brain cortical surfaces; landmark matching; optimized harmonic registration; surface registration
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Imaging Science & Photographic Technology; Mathematics; Mathematics, Applied

Last updated on 2021-09-01 at 00:56