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Brain Image Analysis using Spherical Splines
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| Project Members |
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Ying He,
Xin Li,
Xianfeng Gu,
Hong Qin
Center for Visual Computing
Department of Computer
Science
State University of New
York at Stony Brook |
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| Abstract: |
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We propose a novel technique based on spherical splines for brain surface representation and analysis. This research is strongly inspired by the fact
that, for brain surfaces, it is both necessary and natural to employ spheres as their natural domains. We develop an automatic and efficient algorithm, which
transforms a brain surface to a single spherical spline whose maximal error deviation from the original data is less than the user-specified tolerance. Compared
to the discrete mesh-based representation, our spherical spline offers a concise (low storage requirement) digital form with
high continuity (C^(n-1) continuity for a degree n spherical spline). Furthermore, this representation enables the accurate evaluation of differential properties, such as curvature, principal direction,
and geodesic, without the need for any numerical approximations. Thus, certain
shape analysis procedures, such as segmentation, gyri and sulci tracing, and 3D
shape matching, can be carried out both robustly and accurately. We conduct
several experiments in order to demonstrate the efficacy of our approach for the
quantitative measurement and analysis of brain surfaces. |
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