Liver segmentation by an active contour model with embedded Gaussian mixture model based classifiers

Yanfeng Shang, Aneta Markova, Rudi Deklerck, Edgard Nyssen, Xin Yang, De Mey Johan

Onderzoeksoutput: Conference paper

8 Citaten (Scopus)

Samenvatting

Automatic liver segmentation is a crucial step for diagnosis and surgery planning. To extract the liver, its tumors and vessels, we developed an active contour model with an embedded classifier, based on a Gaussian mixture model fitted to the intensity distribution of the medical image. The difference between the maximum membership of the intensities belonging to the classes of the object and those of the background, is included as an extra speed propagation term in the active contour model. An additional speed controlling term slows down the evolution of the active contour when it approaches an edge, making it quickly convergent to the ideal object. The developed model has been applied to liver segmentation. Some comparisons are made between the Geodesic Active Contour, C-V (active contour without edges) and our model. As the experiments show, our model is accurate, flexible and suited to extract objects surrounded by a complicated background.
Originele taal-2English
Titel772313
RedacteurenPeter Schelkens, Touradj Ebrahimi, Gabriel Cristóbal, Frédéric Truchetet, Pasi Saarikko
UitgeverijSPIE
Pagina's7-772313
Aantal pagina's4
Volume7723
ISBN van geprinte versie978-0-8194-8196-2
StatusPublished - 5 mei 2010
EvenementUnknown - Stockholm, Sweden
Duur: 21 sep 200925 sep 2009

Publicatie series

NaamOptics, Photonics, and Digital Technologies for Multimedia Applications
Nummer1

Conference

ConferenceUnknown
Land/RegioSweden
StadStockholm
Periode21/09/0925/09/09

Bibliografische nota

Peter Schelkens, Touradj Ebrahimi, Gabriel Cristóbal, Frédéric Truchetet, Pasi Saarikko

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