Application of statistical inference in medical images

Gobert Lee; Mariusz Bajger

Application of statistical inference in medical images

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

Abstract

Image segmentation is the first step in a number of research fields such as image processing, computer vision and machine learning. Image segmentation can be roughly presented as the grouping of individual image pixels into (meaningful/useful) partition of regions or objects. Although a large number of algorithms and approaches have been proposed, automated image segmentation continues to be a tantalizing and challenging problem. In this paper, we look at image segmentation as an inference problem and describe the Statistical Region Merging technique (Nock et al; 2004). Applications such as CT and mammographic image segmentation will be used for illustration.

Original language	English
Title of host publication	ANZIAM 2014
Publisher	Australian and New Zealand Industrial and Applied Mathematics
Pages	62
Number of pages	1
ISBN (Electronic)	978-0-473-27350-7
ISBN (Print)	978-0-473-27349-1
Publication status	Published - 2 Feb 2014
Event	Australian and New Zealand Industrial and Applied Mathematics 2014 Conference - Rotorua, New Zealand Duration: 2 Feb 2014 → 6 Feb 2014

Conference

Conference	Australian and New Zealand Industrial and Applied Mathematics 2014 Conference
Abbreviated title	ANZIAM 2014
Country/Territory	New Zealand
City	Rotorua,
Period	2/02/14 → 6/02/14

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https://www.anziam.org.au/tiki-download_file.php?fileId=128Licence: In Copyright

Cite this

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title = "Application of statistical inference in medical images",

abstract = "Image segmentation is the first step in a number of research fields such as image processing, computer vision and machine learning. Image segmentation can be roughly presented as the grouping of individual image pixels into (meaningful/useful) partition of regions or objects. Although a large number of algorithms and approaches have been proposed, automated image segmentation continues to be a tantalizing and challenging problem. In this paper, we look at image segmentation as an inference problem and describe the Statistical Region Merging technique (Nock et al; 2004). Applications such as CT and mammographic image segmentation will be used for illustration.",

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AU - Lee, Gobert

AU - Bajger, Mariusz

PY - 2014/2/2

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N2 - Image segmentation is the first step in a number of research fields such as image processing, computer vision and machine learning. Image segmentation can be roughly presented as the grouping of individual image pixels into (meaningful/useful) partition of regions or objects. Although a large number of algorithms and approaches have been proposed, automated image segmentation continues to be a tantalizing and challenging problem. In this paper, we look at image segmentation as an inference problem and describe the Statistical Region Merging technique (Nock et al; 2004). Applications such as CT and mammographic image segmentation will be used for illustration.

AB - Image segmentation is the first step in a number of research fields such as image processing, computer vision and machine learning. Image segmentation can be roughly presented as the grouping of individual image pixels into (meaningful/useful) partition of regions or objects. Although a large number of algorithms and approaches have been proposed, automated image segmentation continues to be a tantalizing and challenging problem. In this paper, we look at image segmentation as an inference problem and describe the Statistical Region Merging technique (Nock et al; 2004). Applications such as CT and mammographic image segmentation will be used for illustration.

M3 - Conference contribution

SN - 978-0-473-27349-1

SP - 62

BT - ANZIAM 2014

PB - Australian and New Zealand Industrial and Applied Mathematics

T2 - Australian and New Zealand Industrial and Applied Mathematics 2014 Conference

Y2 - 2 February 2014 through 6 February 2014

ER -

Application of statistical inference in medical images

Abstract

Conference

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