• Journal of KIISE:Computer Systems and Theory
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• v.34 no.1
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• pp.28-37
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• 2007

This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching cube algorithm, our method does not extract iso-density surface(isosurface) directly from the voxels but calculates the iso-density point(isopoint) first. After building the relatively coarse initial mesh by the Cell-boundary algorithm approximating the isosurface, it produces the final isosurface by iteratively shrinking and smoothing the initial mesh. Comparing with the Marching Cube algorithm, our method is robust and does not make any crack in resulting surface model. Furthermore, the proposed method surmounts the O(1)-adjacency limitation of MC in defining the isopoints by permitting the O(2) and O(3)-adjacent isopoints in surface reconstruction, and can produce more accurate isosurface. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.511-520
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• 2009

In this paper, we present a new iso-density surface reconstruction scheme based on a hierarchy on the input volume data and the output mesh data. From the input volume data, we construct a hierarchy of volumes, called a volume pyramid, based on a 3D dilation filter. After constructing the volume pyramid, we extract a coarse base mesh from the coarsest resolution of the pyramid with the Cell-boundary representation scheme. We iteratively fit this mesh to the iso-points extracted from the volume data under O(3)-adjacency constraint. For the surface fitting, the shrinking process and the smoothing process are adopted as in the SWIS (Shrink-wrapped isosurface) algorithm[6], and we subdivide the mesh to be able to reconstruct fine detail of the isosurface. The advantage of our method is that it generates a mesh which can be utilized by several multiresolution algorithms such as compression and progressive transmission.