Dense Iterative Contextual Pixel Classification using Kriging

Melanie Ganz; Marco Loog; Sami Brandt; Mads Nielsen

doi:10.1109/CVPR.2009.5204055

Dense Iterative Contextual Pixel Classification using Kriging

Melanie Ganz, Marco Loog, Sami Brandt, Mads Nielsen

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.

Originalsprog	Engelsk
Titel	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009.
Antal sider	7
Publikationsdato	2009
Sider	87-93
ISBN (Trykt)	978-1-4244-3994-2
DOI	https://doi.org/10.1109/CVPR.2009.5204055
Status	Udgivet - 2009
Begivenhed	IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2009) - Miami Beach, USA Varighed: 20 jun. 2009 → 25 jun. 2009 Konferencens nummer: 10

Konference

Konference	IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2009)
Nummer	10
Land/Område	USA
By	Miami Beach
Periode	20/06/2009 → 25/06/2009

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10.1109/CVPR.2009.5204055

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Citationsformater

Ganz, M, Loog, M, Brandt, S & Nielsen, M 2009, Dense Iterative Contextual Pixel Classification using Kriging. i IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. . s. 87-93, IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2009), Miami Beach, USA, 20/06/2009. https://doi.org/10.1109/CVPR.2009.5204055

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abstract = "In medical applications, segmentation has become an ever more important task. One of the competitive schemes toperform 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., iterativecontextual pixel classification, iterated conditional modes, and other approaches related to Markov random fields. Aproblem of these methods, however, is their computational complexity, especially when dealing with high-resolutionimages in which relatively long range interactions may play a role. We propose a new method based on Kriging thatmakes it possible to include such long range interactions, while keeping the computations manageable when dealingwith large medical images.",

author = "Melanie Ganz and Marco Loog and Sami Brandt and Mads Nielsen",

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doi = "10.1109/CVPR.2009.5204055",

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AU - Loog, Marco

AU - Brandt, Sami

AU - Nielsen, Mads

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N2 - In medical applications, segmentation has become an ever more important task. One of the competitive schemes toperform 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., iterativecontextual pixel classification, iterated conditional modes, and other approaches related to Markov random fields. Aproblem of these methods, however, is their computational complexity, especially when dealing with high-resolutionimages in which relatively long range interactions may play a role. We propose a new method based on Kriging thatmakes it possible to include such long range interactions, while keeping the computations manageable when dealingwith large medical images.

AB - In medical applications, segmentation has become an ever more important task. One of the competitive schemes toperform 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., iterativecontextual pixel classification, iterated conditional modes, and other approaches related to Markov random fields. Aproblem of these methods, however, is their computational complexity, especially when dealing with high-resolutionimages in which relatively long range interactions may play a role. We propose a new method based on Kriging thatmakes it possible to include such long range interactions, while keeping the computations manageable when dealingwith large medical images.

U2 - 10.1109/CVPR.2009.5204055

DO - 10.1109/CVPR.2009.5204055

M3 - Article in proceedings

SN - 978-1-4244-3994-2

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EP - 93

BT - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009.

T2 - IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2009)

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ER -

Dense Iterative Contextual Pixel Classification using Kriging

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