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Selected Readings in Vision and Graphics
edited by Luc Van Gool, Gábor Székely,
Markus Gross, Bernt Schiele
Volume 67
Ekaterina
Mishina
Predictive
Properties of
Statistical Shape Models
2011. XVI, 102
pages. EUR 64,00.
ISBN
978-3-86628-387-9
Shape
modeling and prediction from incomplete observation
is a key problem in numerous clinical applications. The specific aim of this
work is to develop a generic prediction framework which takes into account the
main sources of uncertainty within the prediction chain and provides a
quantitative evaluation of the prediction accuracy.
The
quality of statistical shape model utilized to learn variability of the target
organ shape is influenced by the quality of the underlying dense correspondence
between the individual shapes of the training set. We propose an extension to
correspondence establishment over a population based on the minimization of the
description length function, which allows considering objects with arbitrary
topology.
Furthermore,
the representativeness of the training data as well as the quality of the
available observation is important for reliable predictions. We propose a
framework which is able to take both related uncertainties into account and allows the derivation of a distribution of
probable shapes given a noisy observation. The reliability of the prediction is
quantified by the confidence region around the predicted shape and can be
conveniently visualized.
About the
author
Ekaterina Mishina
obtained her M.Sc. (Dipl.) degree in Mathematics and Applied Mathematics from
the Lomonosov Moscow State University, Russia, in
2003. After graduation she was working for two years as a software engineer in
a company developing microprocessors. From 2006 to 2010, she was research
assistant and Ph.D. student at the Computer Vision Laboratory of ETH
Keywords: shape modeling, statistical
shape models, prediction accuracy, topology, organ´s shape.
Reihe " Selected Readings in Vision and Graphics
" im Hartung-Gorre Verlag
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