| Abstract |
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Variational implicit surfaces have been widely used in
computer graphics and animation and generally provide
solutions for uniformly distributed sparse data. The fields
of medicine and biology increasingly rely on accurate
three-dimensional reconstructions. Despite the fact that
biological structures fit perfectly within the scope of
variational implicit interpolation (smooth closed
surfaces), the data to model in these fields constitute a
computational challenge (sparse non-homogeneous data,
high curvatures). Several parametric and simplicial
approaches have been presented[1, 2]. This paper
presents an approach for creating and optimizing 3D
reconstructions based on variational implicit surfaces for
a dataset consisting of serial parallel sections.
Preliminary results and perspectives are presented.
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 Additional Information
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Citation:
Jean-Marie Bouteiller, Michel Baudry,
"Neuroanatomical Imaging: Constrained 3D Reconstruction Using Variational Implicit Techniques,"
3dpvt,
p. 62,
First International Symposium on 3D Data Processing Visualization and Transmission (3DPVT'02),
2002
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