@Article{SergioVera2013,
author="Sergio Vera
and Debora Gil
and Agnes Borras
and Marius George Linguraru
and Miguel Angel Gonzalez Ballester",
editor="Mubarak Shah",
title="Geometric Steerable Medial Maps",
journal="Machine Vision and Applications",
year="2013",
publisher="Springer Berlin Heidelberg",
volume="24",
number="6",
pages="1255--1266",
optkeywords="Medial Representations",
optkeywords="Medial Manifolds Comparation",
optkeywords="Surface",
optkeywords="Reconstruction",
abstract="In order to provide more intuitive and easily interpretable representations of complex shapes/organs, medial manifolds should reach a compromise between simplicity in geometry and capability for restoring the anatomy/shape of the organ/volume. Existing morphological methods show excellent results when applied to 2D objects, but their quality drops across dimensions.This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoids degenerated medial axis segments. Second, we introduce a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to syn- thetic shapes of known medial geometry. We also show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume.",
optnote="IAM; 605.203; 600.060; 600.044",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2192), last updated on Tue, 25 Feb 2020 13:07:22 +0100",
issn="0932-8092",
doi="10.1007/s00138-013-0490-4",
opturl="http://www.springer.com/computer/image+processing/journal/138",
file=":http://refbase.cvc.uab.es/files/VGB2013.pdf:PDF"
}