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Automatic framework for the detection of coronary artery calcification in IVUS images

The paper propose an automatic framework for the detection of coronary artery calcification in intravascular ultrasound (IVUS) images using texture analysis method. The texture features used is called Histogram of Equivalent Patterns (HEPs) Features. Experiments was conducted using 2175 IVUS images,...

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Main Authors: Mohammad, S., Sofian, H., Mohd. Noor, Norliza
Format: Article
Published: The Mattingley Publishing Co., Inc. 2020
Subjects:
T58.6-58.62 Management information systems
Online Access:http://eprints.utm.my/id/eprint/91273/
https://testmagzine.biz/index.php/testmagzine/article/view/5103
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spelling my.utm.912732021-06-30T12:07:18Z http://eprints.utm.my/id/eprint/91273/ Automatic framework for the detection of coronary artery calcification in IVUS images Mohammad, S. Sofian, H. Mohd. Noor, Norliza T58.6-58.62 Management information systems The paper propose an automatic framework for the detection of coronary artery calcification in intravascular ultrasound (IVUS) images using texture analysis method. The texture features used is called Histogram of Equivalent Patterns (HEPs) Features. Experiments was conducted using 2175 IVUS images, 530 with calcification plague and 1645 without calci-fication plague. The images are from dataset B of MICCAI challenge 2011. The classifier used is 1-NN classifier. A 2-fold cross-validation process is applied to the IVUS image database to evaluate the performance of the proposed framework. The highest accuracy obtained is 95.89 %, using a variant of Com-pleted Local Binary Patterns (CLBP) descriptors as the features. The Mattingley Publishing Co., Inc. 2020-04 Article PeerReviewed Mohammad, S. and Sofian, H. and Mohd. Noor, Norliza (2020) Automatic framework for the detection of coronary artery calcification in IVUS images. Test Engineering and Management, 83 . pp. 7984-7992. ISSN 0193-4120 https://testmagzine.biz/index.php/testmagzine/article/view/5103
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 T58.6-58.62 Management information systems
spellingShingle T58.6-58.62 Management information systems
Mohammad, S.
Sofian, H.
Mohd. Noor, Norliza
Automatic framework for the detection of coronary artery calcification in IVUS images
description The paper propose an automatic framework for the detection of coronary artery calcification in intravascular ultrasound (IVUS) images using texture analysis method. The texture features used is called Histogram of Equivalent Patterns (HEPs) Features. Experiments was conducted using 2175 IVUS images, 530 with calcification plague and 1645 without calci-fication plague. The images are from dataset B of MICCAI challenge 2011. The classifier used is 1-NN classifier. A 2-fold cross-validation process is applied to the IVUS image database to evaluate the performance of the proposed framework. The highest accuracy obtained is 95.89 %, using a variant of Com-pleted Local Binary Patterns (CLBP) descriptors as the features.
format Article
author Mohammad, S.
Sofian, H.
Mohd. Noor, Norliza
author_facet Mohammad, S.
Sofian, H.
Mohd. Noor, Norliza
author_sort Mohammad, S.
title Automatic framework for the detection of coronary artery calcification in IVUS images
title_short Automatic framework for the detection of coronary artery calcification in IVUS images
title_full Automatic framework for the detection of coronary artery calcification in IVUS images
title_fullStr Automatic framework for the detection of coronary artery calcification in IVUS images
title_full_unstemmed Automatic framework for the detection of coronary artery calcification in IVUS images
title_sort automatic framework for the detection of coronary artery calcification in ivus images
publisher The Mattingley Publishing Co., Inc.
publishDate 2020
url http://eprints.utm.my/id/eprint/91273/
https://testmagzine.biz/index.php/testmagzine/article/view/5103
_version_ 1705056690201690112
score 13.211869

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