Dense Iterative Contextual Pixel Classification using Kriging

Melanie Ganz, Marco Loog, Sami Brandt, Mads Nielsen

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

1 Citationer (Scopus)

Abstract

In medical applications, segmentation has become an ever more important task. One of the competitive schemes to
perform such segmentation is by means of pixel classification. Simple pixel-based classification schemes can be improved by incorporating contextual label information. Various methods have been proposed to this end, e.g., iterative
contextual pixel classification, iterated conditional modes, and other approaches related to Markov random fields. A
problem of these methods, however, is their computational complexity, especially when dealing with high-resolution
images in which relatively long range interactions may play a role. We propose a new method based on Kriging that
makes it possible to include such long range interactions, while keeping the computations manageable when dealing
with large medical images.
OriginalsprogEngelsk
TitelIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009.
Antal sider7
Publikationsdato2009
Sider87-93
ISBN (Trykt)978-1-4244-3994-2
DOI
StatusUdgivet - 2009
BegivenhedIEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2009) - Miami Beach, USA
Varighed: 20 jun. 200925 jun. 2009
Konferencens nummer: 10

Konference

KonferenceIEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2009)
Nummer10
Land/OmrådeUSA
ByMiami Beach
Periode20/06/200925/06/2009

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