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research article

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

Türetken, Engin  
•
Benmansour, Fethallah  
•
Andres, Bjoern
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2016
IEEE Transactions On Pattern Analysis And Machine Intelligence (PAMI)

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques.

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Type
research article
DOI
10.1109/Tpami.2016.2519025
Web of Science ID

WOS:000387984700013

Author(s)
Türetken, Engin  
Benmansour, Fethallah  
Andres, Bjoern
Glowacki, Przemyslaw Rafal  
Pfister, Hanspeter
Fua, Pascal  
Date Issued

2016

Published in
IEEE Transactions On Pattern Analysis And Machine Intelligence (PAMI)
Volume

38

Issue

12

Start page

2515

Subjects

curvilinear networks

•

tubular structures

•

curvilinear structures

•

automated reconstruction

•

integer programming

•

path classification

•

minimum arborescence.

URL

URL

http://cvlab.epfl.ch/research/medical/lm
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CVLAB  
Available on Infoscience
September 10, 2014
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/106841
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