On Modal Modeling for Medical Images Underconstrained Shape Description and Data Compressio.pdf
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On Modal Modeling for Medical Images Underconstrained Shape Description and Data Compressio
M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 275
Appeared in Proc. of the IEEE Workshop on Biomedical Image Analysis, Seattle, June 1994 (pp. 70–79).
On Modal Modeling for Medical Images:
Underconstrained Shape Description and Data Compression
Stan Sclaroff and Alex P. Pentland
Perceptual Computing Section, The MIT Media Laboratory
20 Ames St., Cambridge MA 02139
Abstract
We have previously described modal analysis, an effi-
cient, physically-based solution for recovering, tracking,
and recognizing solid models from 2-D and 3-D sensor
data. The underlying representation consists of two levels:
modal deformations, which describe the overall shape of a
solid, and displacement maps, which employ a multiscale
wavelet representation to provide local and fine surface de-
tail. This paper addresses the problem of recovering modal
models in the underconstrained case of fitting a 3-D model
to contours found in medical slice and X-ray data. We will
describe an extension which can be used to incorporate
measurement uncertainty while estimating the modal de-
formation parameters. Finally, we give details about how
to compress dense 3-D point data from surfaces, by use of
displacement maps and wavelets.
1 Introduction
Contours present an interesting problem in fitting de-
formable solids to medical data: when a 3-D model is
recovered from contours extracted from 2-D slice data, the
model fitting algorithm must make an intelligent guess at
what’s going on in the third dimension — things are uncer-
tain in the dimension perpendicular to a slice. This classic
machine vision problem also arises when fitting partial or
occluded data: how can we fill in information where we
have no measurements, or noisy measurements?
To solve these problems, we should carry measurement
uncertainty along in our solid model recovery framework,
recovering not only model parameters, but also their distri-
butions [7; 27]. Such a framework can be used to set limits
on our model
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