An Automated Volumetric Segmentation System Combining Multiscale and Statistical Reasoning

David W.G. Montgomery; Abbes Amira; Fionn Murtagh

doi:10.1109/ISCAS.2005.1465455

An Automated Volumetric Segmentation System Combining Multiscale and Statistical Reasoning

David W.G. Montgomery, Abbes Amira, Fionn Murtagh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

An automated volumetric image segmentation algorithm is proposed. This method is fast and unsupervised, automatically estimating required parameters including optimal segment number selection using Bayesian inference. In the wavelet domain, Gaussian Mixture Modeling (GMM) is used to achieve a baseline scene estimate. This estimate is then refined to consider spatial correlations using a Markov Random Field Model (MRFM). The application of this system to three-dimensional biomedical image volumes is discussed. This approach delivers promising results in terms of the identification of inherent image features.

Original language	English
Title of host publication	IEEE International Symposium on Circuits and Systems (ISCAS 2005)
Publisher	IEEE
Pages	3789-3792
Number of pages	4
ISBN (Print)	0780388348, 9780780388345
DOIs	https://doi.org/10.1109/ISCAS.2005.1465455
Publication status	Published - 25 Jul 2005
Externally published	Yes
Event	IEEE International Symposium on Circuits and Systems 2005 - International Conference Center Kobe & Kobe Portopia Hotel, Kobe, Japan Duration: 23 May 2005 → 26 May 2005 https://ieeexplore.ieee.org/document/1407961

Publication series

Name	IEEE International Symposium on Circuits and Systems
Publisher	IEEE
ISSN (Print)	0271-4310
ISSN (Electronic)	2158-1525

Conference

Conference	IEEE International Symposium on Circuits and Systems 2005
Abbreviated title	ISCAS 2005
Country/Territory	Japan
City	Kobe
Period	23/05/05 → 26/05/05
Internet address	https://ieeexplore.ieee.org/document/1407961

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10.1109/ISCAS.2005.1465455Licence: Unspecified

Cite this

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title = "An Automated Volumetric Segmentation System Combining Multiscale and Statistical Reasoning",

abstract = "An automated volumetric image segmentation algorithm is proposed. This method is fast and unsupervised, automatically estimating required parameters including optimal segment number selection using Bayesian inference. In the wavelet domain, Gaussian Mixture Modeling (GMM) is used to achieve a baseline scene estimate. This estimate is then refined to consider spatial correlations using a Markov Random Field Model (MRFM). The application of this system to three-dimensional biomedical image volumes is discussed. This approach delivers promising results in terms of the identification of inherent image features.",

keywords = "Markov processes, Image segmentation, SAR image",

author = "Montgomery, {David W.G.} and Abbes Amira and Fionn Murtagh",

year = "2005",

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language = "English",

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series = "IEEE International Symposium on Circuits and Systems",

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booktitle = "IEEE International Symposium on Circuits and Systems (ISCAS 2005)",

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note = "IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 ; Conference date: 23-05-2005 Through 26-05-2005",

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Montgomery, DWG, Amira, A & Murtagh, F 2005, An Automated Volumetric Segmentation System Combining Multiscale and Statistical Reasoning. in IEEE International Symposium on Circuits and Systems (ISCAS 2005). IEEE International Symposium on Circuits and Systems, IEEE, pp. 3789-3792, IEEE International Symposium on Circuits and Systems 2005, Kobe, Japan, 23/05/05. https://doi.org/10.1109/ISCAS.2005.1465455

An Automated Volumetric Segmentation System Combining Multiscale and Statistical Reasoning. / Montgomery, David W.G.; Amira, Abbes; Murtagh, Fionn.

IEEE International Symposium on Circuits and Systems (ISCAS 2005). IEEE, 2005. p. 3789-3792 (IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Amira, Abbes

AU - Murtagh, Fionn

PY - 2005/7/25

Y1 - 2005/7/25

N2 - An automated volumetric image segmentation algorithm is proposed. This method is fast and unsupervised, automatically estimating required parameters including optimal segment number selection using Bayesian inference. In the wavelet domain, Gaussian Mixture Modeling (GMM) is used to achieve a baseline scene estimate. This estimate is then refined to consider spatial correlations using a Markov Random Field Model (MRFM). The application of this system to three-dimensional biomedical image volumes is discussed. This approach delivers promising results in terms of the identification of inherent image features.

AB - An automated volumetric image segmentation algorithm is proposed. This method is fast and unsupervised, automatically estimating required parameters including optimal segment number selection using Bayesian inference. In the wavelet domain, Gaussian Mixture Modeling (GMM) is used to achieve a baseline scene estimate. This estimate is then refined to consider spatial correlations using a Markov Random Field Model (MRFM). The application of this system to three-dimensional biomedical image volumes is discussed. This approach delivers promising results in terms of the identification of inherent image features.

KW - Markov processes

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KW - SAR image

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SN - 9780780388345

T3 - IEEE International Symposium on Circuits and Systems

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BT - IEEE International Symposium on Circuits and Systems (ISCAS 2005)

PB - IEEE

T2 - IEEE International Symposium on Circuits and Systems 2005

Y2 - 23 May 2005 through 26 May 2005

ER -

An Automated Volumetric Segmentation System Combining Multiscale and Statistical Reasoning

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