Abstract #4299
Universal shape interpolation using the Radon transform
Peter Adany 1 , Phil Lee 2 , Douglas R. Denney 3 , Sharon G. Lynch 4 , and In-Young Choi 4
1
Hoglund Brain Imaging Center, University of
Kansas Medical Center, Kansas City, KS, United States,
2
Department
of Molecular and Integrative Physiology, University of
Kansas Medical Center, Kansas City, KS, United States,
3
Department
of Clinical Psychology, University of Kansas, Lawrence,
KS, United States,
4
Department
of Neurology, University of Kansas Medical Center,
Kansas City, KS, United States
Processing of region of interest (ROI) shapes for
medical image analysis is often encumbered by the poor
results of intensity interpolation when resampling
axially from low to high resolution, e.g. when editing
ROIs using original images with few slices prior to
resampling to high resolution ROI shape images. A
straightforward shape interpolation algorithm is
proposed based on the Radon transform and filtered back
projection. We investigate the capability of this
algorithm to preserve fine image details and to
transversally merge information between the adjacent
slices. Results show interpolating characteristics
greatly superior to intensity interpolation.
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