Optimal Graph Based Segmentation Using Flow Lines with Application to Airway Wall Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2011

AUTHORS

Jens Petersen , Mads Nielsen , Pechin Lo , Zaigham Saghir , Asger Dirksen , Marleen de Bruijne

ABSTRACT

This paper introduces a novel optimal graph construction method that is applicable to multi-dimensional, multi-surface segmentation problems. Such problems are often solved by refining an initial coarse surface within the space given by graph columns. Conventional columns are not well suited for surfaces with high curvature or complex shapes but the proposed columns, based on properly generated flow lines, which are non-intersecting, guarantee solutions that do not self-intersect and are better able to handle such surfaces. The method is applied to segment human airway walls in computed tomography images. Comparison with manual annotations on 649 cross-sectional images from 15 different subjects shows significantly smaller contour distances and larger area of overlap than are obtained with recently published graph based methods. Airway abnormality measurements obtained with the method on 480 scan pairs from a lung cancer screening trial are reproducible and correlate significantly with lung function. More... »

PAGES

49-60

References to SciGraph publications

  • 2009. Airway Tree Extraction with Locally Optimal Paths in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 2002. Optimal Net Surface Problems with Applications in AUTOMATA, LANGUAGES AND PROGRAMMING
  • 2009. Electric Field Theory Motivated Graph Construction for Optimal Medical Image Segmentation in GRAPH-BASED REPRESENTATIONS IN PATTERN RECOGNITION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-22092-0_5

    DOI

    http://dx.doi.org/10.1007/978-3-642-22092-0_5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1049562386

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21761645


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