Cover Image

Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM

Virtual Histology Intravascular Ultrasound (VH-IVUS) is a clinically available for visualizing color coded of coronary artery plaque. However, current VH-IVUS image processing techniques have not considered the combinations of features to identify vulnerable plaque. This paper presents a new method...

Full description

Saved in:
Bibliographic Details
Main Authors: Rezaei, Z., Selamat, A., Taki, A., Mohd. Rahim, M. S., Abdul Kadir, M. R.
Format: Article
Published: IOS Press 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/59219/
https://doi.org/10.3233/978-1-61499-522-7-149
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.59219
record_format eprints
spelling my.utm.592192022-01-26T02:49:47Z http://eprints.utm.my/id/eprint/59219/ Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM Rezaei, Z. Selamat, A. Taki, A. Mohd. Rahim, M. S. Abdul Kadir, M. R. QA75 Electronic computers. Computer science Virtual Histology Intravascular Ultrasound (VH-IVUS) is a clinically available for visualizing color coded of coronary artery plaque. However, current VH-IVUS image processing techniques have not considered the combinations of features to identify vulnerable plaque. This paper presents a new method for classification of TCFA (thin-cap fibroatheromas) and Non-TCFA plaque based on combined features using the VH-IVUS images using support vector machine (SVM). The proposed method is applied to 546 in-vivo VH-IVUS images. Results proved the dominance of our proposed method with accuracy rates of 98.15% for TCFA. IOS Press 2015 Article PeerReviewed Rezaei, Z. and Selamat, A. and Taki, A. and Mohd. Rahim, M. S. and Abdul Kadir, M. R. (2015) Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM. Frontiers in Artificial Intelligence and Applications, 276 . pp. 149-156. ISSN 0922-6389 https://doi.org/10.3233/978-1-61499-522-7-149 DOI: 10.3233/978-1-61499-522-7-149
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Rezaei, Z.
Selamat, A.
Taki, A.
Mohd. Rahim, M. S.
Abdul Kadir, M. R.
Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM
description Virtual Histology Intravascular Ultrasound (VH-IVUS) is a clinically available for visualizing color coded of coronary artery plaque. However, current VH-IVUS image processing techniques have not considered the combinations of features to identify vulnerable plaque. This paper presents a new method for classification of TCFA (thin-cap fibroatheromas) and Non-TCFA plaque based on combined features using the VH-IVUS images using support vector machine (SVM). The proposed method is applied to 546 in-vivo VH-IVUS images. Results proved the dominance of our proposed method with accuracy rates of 98.15% for TCFA.
format Article
author Rezaei, Z.
Selamat, A.
Taki, A.
Mohd. Rahim, M. S.
Abdul Kadir, M. R.
author_facet Rezaei, Z.
Selamat, A.
Taki, A.
Mohd. Rahim, M. S.
Abdul Kadir, M. R.
author_sort Rezaei, Z.
title Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM
title_short Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM
title_full Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM
title_fullStr Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM
title_full_unstemmed Detection of vulnerable plaque in virtual histology intravascular ultrasound images using SVM
title_sort detection of vulnerable plaque in virtual histology intravascular ultrasound images using svm
publisher IOS Press
publishDate 2015
url http://eprints.utm.my/id/eprint/59219/
https://doi.org/10.3233/978-1-61499-522-7-149
_version_ 1724073240209391616
score 13.159267

Similar Items