Cross-correlation Template Matching for Liver Localisation in Computed Tomography

Patrick Leyden; Martin O'Connell; Derek Greene; Kathleen Curran

doi:10.21427/8fgf-y086

Cross-correlation Template Matching for Liver Localisation in Computed Tomography

Patrick Leyden
, Martin O'Connell
, Derek Greene
, Kathleen Curran

Research output: Contribution to conference › Paper › peer-review

Abstract

Many of the current approaches to automatic organ localisation in medical imaging require a large amount of labelled patient data to train systems to accurately identify specific anatomical features. Cross- Correlation, also known as template matching, is a statistical method of assessing the similarity between a template image and a target image. This method has been modified and presented here to localize the liver in Computed Tomography volume images in the Coronal and Sagital planes to achieve a mean positioning error of approximately 11 mm and 20 mm respectively based on between 1 and 25 datasets to create the template liver.

Original language	English
DOIs	https://doi.org/10.21427/8fgf-y086
Publication status	Published - 1 Jan 2019
Externally published	Yes
Event	IMVIP 2019: Irish Machine Vision & Image Processing - Technological University Dublin, Dublin, Ireland Duration: 28 Aug 2019 → 30 Aug 2019

Conference

Conference	IMVIP 2019: Irish Machine Vision & Image Processing
Country/Territory	Ireland
City	Dublin
Period	28/08/19 → 30/08/19

Keywords

automatic organ localisation
medical imaging
template matching
liver
Computed Tomography
Coronal plane
Sagital plane
positioning error

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10.21427/8fgf-y086Licence: CC BY-NC-SA

Cite this

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title = "Cross-correlation Template Matching for Liver Localisation in Computed Tomography",

abstract = "Many of the current approaches to automatic organ localisation in medical imaging require a large amount of labelled patient data to train systems to accurately identify specific anatomical features. Cross- Correlation, also known as template matching, is a statistical method of assessing the similarity between a template image and a target image. This method has been modified and presented here to localize the liver in Computed Tomography volume images in the Coronal and Sagital planes to achieve a mean positioning error of approximately 11 mm and 20 mm respectively based on between 1 and 25 datasets to create the template liver.",

keywords = "automatic organ localisation, medical imaging, template matching, liver, Computed Tomography, Coronal plane, Sagital plane, positioning error",

author = "Patrick Leyden and Martin O'Connell and Derek Greene and Kathleen Curran",

year = "2019",

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day = "1",

doi = "10.21427/8fgf-y086",

language = "English",

note = "IMVIP 2019: Irish Machine Vision \& Image Processing ; Conference date: 28-08-2019 Through 30-08-2019",

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TY - CONF

T1 - Cross-correlation Template Matching for Liver Localisation in Computed Tomography

AU - Leyden, Patrick

AU - O'Connell, Martin

AU - Greene, Derek

AU - Curran, Kathleen

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Many of the current approaches to automatic organ localisation in medical imaging require a large amount of labelled patient data to train systems to accurately identify specific anatomical features. Cross- Correlation, also known as template matching, is a statistical method of assessing the similarity between a template image and a target image. This method has been modified and presented here to localize the liver in Computed Tomography volume images in the Coronal and Sagital planes to achieve a mean positioning error of approximately 11 mm and 20 mm respectively based on between 1 and 25 datasets to create the template liver.

AB - Many of the current approaches to automatic organ localisation in medical imaging require a large amount of labelled patient data to train systems to accurately identify specific anatomical features. Cross- Correlation, also known as template matching, is a statistical method of assessing the similarity between a template image and a target image. This method has been modified and presented here to localize the liver in Computed Tomography volume images in the Coronal and Sagital planes to achieve a mean positioning error of approximately 11 mm and 20 mm respectively based on between 1 and 25 datasets to create the template liver.

KW - automatic organ localisation

KW - medical imaging

KW - template matching

KW - liver

KW - Computed Tomography

KW - Coronal plane

KW - Sagital plane

KW - positioning error

U2 - 10.21427/8fgf-y086

DO - 10.21427/8fgf-y086

M3 - Paper

T2 - IMVIP 2019: Irish Machine Vision & Image Processing

Y2 - 28 August 2019 through 30 August 2019

ER -

Cross-correlation Template Matching for Liver Localisation in Computed Tomography

Abstract

Conference

Keywords

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Cite this