We are interested in establishing ground truth data for validating morphology measurements of human knee
cartilage from MR imaging. One promising approach is to compare the high-accuracy 3D laser scans of dissected
cadaver knees before and after the dissolution of their cartilage. This requires an accurate and reliable method
to fuse the individual laser scans from multiple views of the cadaver knees. Unfortunately existing methods
using Iterative Closest Point (ICP) algorithm from off-the-shell packages often yield unreliable fusion results.
We identify two major sources of variation: (i) the noise in depth measurements of the laser scans is significantly
high and (ii) the use of point-to-point correspondence in ICP is not suitable due to sampling variation in the
laser scans. We resolve the first problem by performing adaptive Gaussian smoothing on each individual laser
scans prior to the fusion. For the second problem, we construct a surface mesh from the point cloud of each scan
and adopt a point-to-mesh ICP scheme for pairwise alignment. The complete surface mesh is constructed by
fusing all the scans in the order maximizing mutual overlaps. In experiments on 6 repeated scanning trials of a
cadaver knee, our approach reduced the alignment error of point-to-point ICP by 30% and reduced coefficient of
variation (CV) of cartilage thickness measurements from 5% down to 1.4%, significantly improving the method's
repeatability.
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