Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database

Filippo Molinari; Christos Loizou; Guang Zeng; Costantinos Pattichis; Marios Pantziaris; William Liboni; Andrew Nicolaides; Jasjit S. Suri

doi:10.1117/12.877131

Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database

Filippo Molinari
, Christos Loizou
, Guang Zeng
, Costantinos Pattichis
, Marios Pantziaris
, William Liboni
, Andrew Nicolaides
, Jasjit S. Suri

Department of Computer Science

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

Abstract

Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX ^1,2), a method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX³), and a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between the LI/MA borders is the IMT estimation. The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation and IMT measurement based on an innovative multi-resolution approach.

Original language	English
Title of host publication	Medical Imaging 2011
Subtitle of host publication	Image Processing
Volume	7962
DOIs	https://doi.org/10.1117/12.877131
Publication status	Published - 2011
Event	Medical Imaging 2011: Image Processing - Lake Buena Vista, FL, United States Duration: 14 Feb 2011 → 16 Feb 2011

Other

Other	Medical Imaging 2011: Image Processing
Country/Territory	United States
City	Lake Buena Vista, FL
Period	14/02/11 → 16/02/11

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10.1117/12.877131

Cite this

Molinari, F., Loizou, C., Zeng, G., Pattichis, C., Pantziaris, M., Liboni, W., Nicolaides, A., & Suri, J. S. (2011). Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database. In Medical Imaging 2011: Image Processing (Vol. 7962). Article 79623T https://doi.org/10.1117/12.877131

@inproceedings{8719ecba060f44de9cc7c2efee35e18c,

title = "Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database",

abstract = "Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX 1,2), a method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX3), and a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between the LI/MA borders is the IMT estimation. The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation and IMT measurement based on an innovative multi-resolution approach.",

author = "Filippo Molinari and Christos Loizou and Guang Zeng and Costantinos Pattichis and Marios Pantziaris and William Liboni and Andrew Nicolaides and Suri, \{Jasjit S.\}",

year = "2011",

doi = "10.1117/12.877131",

language = "English",

isbn = "9780819485045",

volume = "7962",

booktitle = "Medical Imaging 2011",

note = "Medical Imaging 2011: Image Processing ; Conference date: 14-02-2011 Through 16-02-2011",

}

Molinari, F, Loizou, C, Zeng, G, Pattichis, C, Pantziaris, M, Liboni, W, Nicolaides, A & Suri, JS 2011, Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database. in Medical Imaging 2011: Image Processing. vol. 7962, 79623T, Medical Imaging 2011: Image Processing, Lake Buena Vista, FL, United States, 14/02/11. https://doi.org/10.1117/12.877131

Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database. / Molinari, Filippo; Loizou, Christos; Zeng, Guang et al.
Medical Imaging 2011: Image Processing. Vol. 7962 2011. 79623T.

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

TY - GEN

T1 - Completely automated multiresolution edge snapper (CAMES)

T2 - Medical Imaging 2011: Image Processing

AU - Molinari, Filippo

AU - Loizou, Christos

AU - Zeng, Guang

AU - Pattichis, Costantinos

AU - Pantziaris, Marios

AU - Liboni, William

AU - Nicolaides, Andrew

AU - Suri, Jasjit S.

PY - 2011

Y1 - 2011

N2 - Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX 1,2), a method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX3), and a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between the LI/MA borders is the IMT estimation. The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation and IMT measurement based on an innovative multi-resolution approach.

AB - Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX 1,2), a method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX3), and a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between the LI/MA borders is the IMT estimation. The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation and IMT measurement based on an innovative multi-resolution approach.

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DO - 10.1117/12.877131

M3 - Conference contribution

AN - SCOPUS:79958015020

SN - 9780819485045

VL - 7962

BT - Medical Imaging 2011

Y2 - 14 February 2011 through 16 February 2011

ER -

Completely automated multiresolution edge snapper (CAMES): A new technique for an accurate carotid ultrasound IMT measurement and its validation on a multi-institutional database

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