Abstract:
This paper presents a volumetric approach to reconstructing a smooth surface
from a sparse set of parallel binary contours, e.g. those produced via
histologic imaging. It creates a volume dataset by interpolating 2D filtered
distance fields. The zero isosurface embedded in the computed volume provides
the desired result. MPU implicit functions are fit to the input contours,
defined as binary images, to produce smooth curves with controllable error
bounds. Full 2D Euclidean distance fields are then calculated from the
implicit curves. A distance-dependent Gaussian filter is applied to the
distance fields to smooth their medial axis discontinuities.
Monotonicity-constraining cubic splines are used to construct smooth, blending
slices between the input slices. A mesh that approximates the zero isosurface
is then extracted from the resulting volume. The effectiveness of the approach
is demonstrated on a number of complex, multi-component contour datasets.
