Segmentation of carotid arteries in CTA images

Richard Beare; Winston Chong; Mandy Ren; Gita Das; Velandai Srikanth; Thanh Phan

doi:10.1109/DICTA.2010.21

Segmentation of carotid arteries in CTA images

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

Abstract

Stenosis of the internal carotid artery (ICA) is implicated in approximately one quarter of stroke cases. The degree of stenosis is currently used to decide whether to undertake a surgical procedure to reduce the risk of further stroke. However it is known that the degree of stenosis is not a good predictor of stroke risk. It is hoped that prediction might be improved by incorporation of other geometric factors. This paper describes a data driven approach using classical methods from the field of mathematical morphology to automatically segment the carotid artery tree in computed tomography angiography (CTA) images following user initialization. The resulting segmentation may be used to characterize the the arterial geometery in a variety of more complex ways than is possible using manual approaches.

Original language	English
Title of host publication	Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010
Pages	69-74
Number of pages	6
DOIs	https://doi.org/10.1109/DICTA.2010.21
Publication status	Published - 2010
Event	Digital Image Computing Techniques and Applications 2010 - Mercure Sydney Hotel, Sydney, Australia Duration: 1 Dec 2010 → 3 Dec 2010 Conference number: 12th https://ieeexplore.ieee.org/xpl/conhome/5689831/proceeding (Proceedings)

Conference

Conference	Digital Image Computing Techniques and Applications 2010
Abbreviated title	DICTA 2010
Country/Territory	Australia
City	Sydney
Period	1/12/10 → 3/12/10
Other	(DICTA) is the main Australian conference on machine vision, image processing, pattern recognition and related areas. Since its establishment, DICTA has been a biannual meeting. In 2008, it turned into an annual conference. It is the conference of the Australian Pattern Recognition Society (APRS).
Internet address	https://ieeexplore.ieee.org/xpl/conhome/5689831/proceeding (Proceedings)

Keywords

Carotid artery
Watershed transform

Access to Document

10.1109/DICTA.2010.21

Cite this

@inproceedings{ed39e9ee3874431b9ce0914c14554dbe,

title = "Segmentation of carotid arteries in CTA images",

abstract = "Stenosis of the internal carotid artery (ICA) is implicated in approximately one quarter of stroke cases. The degree of stenosis is currently used to decide whether to undertake a surgical procedure to reduce the risk of further stroke. However it is known that the degree of stenosis is not a good predictor of stroke risk. It is hoped that prediction might be improved by incorporation of other geometric factors. This paper describes a data driven approach using classical methods from the field of mathematical morphology to automatically segment the carotid artery tree in computed tomography angiography (CTA) images following user initialization. The resulting segmentation may be used to characterize the the arterial geometery in a variety of more complex ways than is possible using manual approaches.",

keywords = "Carotid artery, Watershed transform",

author = "Richard Beare and Winston Chong and Mandy Ren and Gita Das and Velandai Srikanth and Thanh Phan",

year = "2010",

doi = "10.1109/DICTA.2010.21",

language = "English",

isbn = "9780769542713",

pages = "69--74",

booktitle = "Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010",

note = "Digital Image Computing Techniques and Applications 2010, DICTA 2010 ; Conference date: 01-12-2010 Through 03-12-2010",

url = "https://ieeexplore.ieee.org/xpl/conhome/5689831/proceeding",

}

TY - GEN

T1 - Segmentation of carotid arteries in CTA images

AU - Beare, Richard

AU - Chong, Winston

AU - Ren, Mandy

AU - Das, Gita

AU - Srikanth, Velandai

AU - Phan, Thanh

N1 - Conference code: 12th

PY - 2010

Y1 - 2010

N2 - Stenosis of the internal carotid artery (ICA) is implicated in approximately one quarter of stroke cases. The degree of stenosis is currently used to decide whether to undertake a surgical procedure to reduce the risk of further stroke. However it is known that the degree of stenosis is not a good predictor of stroke risk. It is hoped that prediction might be improved by incorporation of other geometric factors. This paper describes a data driven approach using classical methods from the field of mathematical morphology to automatically segment the carotid artery tree in computed tomography angiography (CTA) images following user initialization. The resulting segmentation may be used to characterize the the arterial geometery in a variety of more complex ways than is possible using manual approaches.

AB - Stenosis of the internal carotid artery (ICA) is implicated in approximately one quarter of stroke cases. The degree of stenosis is currently used to decide whether to undertake a surgical procedure to reduce the risk of further stroke. However it is known that the degree of stenosis is not a good predictor of stroke risk. It is hoped that prediction might be improved by incorporation of other geometric factors. This paper describes a data driven approach using classical methods from the field of mathematical morphology to automatically segment the carotid artery tree in computed tomography angiography (CTA) images following user initialization. The resulting segmentation may be used to characterize the the arterial geometery in a variety of more complex ways than is possible using manual approaches.

KW - Carotid artery

KW - Watershed transform

UR - https://www.scopus.com/pages/publications/79951620946

U2 - 10.1109/DICTA.2010.21

DO - 10.1109/DICTA.2010.21

M3 - Conference Paper

AN - SCOPUS:79951620946

SN - 9780769542713

SP - 69

EP - 74

BT - Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010

T2 - Digital Image Computing Techniques and Applications 2010

Y2 - 1 December 2010 through 3 December 2010

ER -

Segmentation of carotid arteries in CTA images

Abstract

Conference

Keywords

Access to Document

Other files and links

Cite this