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.
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