Dense Iterative Contextual Pixel Classification using Kriging

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Dense Iterative Contextual Pixel Classification using Kriging. / Ganz, Melanie; Loog, Marco; Brandt, Sami; Nielsen, Mads.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. . 2009. s. 87-93.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

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

APA

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) https://doi.org/10.1109/CVPR.2009.5204055

Vancouver

Ganz M, Loog M, Brandt S, Nielsen M. Dense Iterative Contextual Pixel Classification using Kriging. I IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. . 2009. s. 87-93 https://doi.org/10.1109/CVPR.2009.5204055

Author

Ganz, Melanie ; Loog, Marco ; Brandt, Sami ; Nielsen, Mads. / Dense Iterative Contextual Pixel Classification using Kriging. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. . 2009. s. 87-93

Bibtex

@inproceedings{ba5aa4e0a2b011debc73000ea68e967b,

title = "Dense Iterative Contextual Pixel Classification using Kriging",

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",

year = "2009",

doi = "10.1109/CVPR.2009.5204055",

language = "English",

isbn = "978-1-4244-3994-2",

pages = "87--93",

booktitle = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009.",

note = "null ; Conference date: 20-06-2009 Through 25-06-2009",

}

RIS

TY - GEN

T1 - Dense Iterative Contextual Pixel Classification using Kriging

AU - Ganz, Melanie

AU - Loog, Marco

AU - Brandt, Sami

AU - Nielsen, Mads

N1 - Conference code: 10

PY - 2009

Y1 - 2009

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

SP - 87

EP - 93

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

Y2 - 20 June 2009 through 25 June 2009

ER -

ID: 14464921