Staff View: Detection of Collaterals from Cone-Beam CT Images in Stroke

Detection of Collaterals from Cone-Beam CT Images in Stroke

Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, a...

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Main Authors:	Abd Aziz, A., Izhar, L.I., Asirvadam, V.S., Tang, T.B., Ajam, A., Omar, Z., Muda, S.
Format:	Article
Institution:	Universiti Teknologi Petronas
Record Id / ISBN-0:	utp-eprints.29582 /
Published:	NLM (Medline) 2021
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122494766&doi=10.3390%2fs21238099&partnerID=40&md5=84a64ebf408bb1596c8f3d879eee1ca6 http://eprints.utp.edu.my/29582/
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id	utp-eprints.29582
recordtype	eprints
spelling	utp-eprints.295822022-03-25T02:09:36Z Detection of Collaterals from Cone-Beam CT Images in Stroke Abd Aziz, A. Izhar, L.I. Asirvadam, V.S. Tang, T.B. Ajam, A. Omar, Z. Muda, S. Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90 and able to detect the collateral and non-collateral vessels from images. NLM (Medline) 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122494766&doi=10.3390%2fs21238099&partnerID=40&md5=84a64ebf408bb1596c8f3d879eee1ca6 Abd Aziz, A. and Izhar, L.I. and Asirvadam, V.S. and Tang, T.B. and Ajam, A. and Omar, Z. and Muda, S. (2021) Detection of Collaterals from Cone-Beam CT Images in Stroke. Sensors (Basel, Switzerland), 21 (23). http://eprints.utp.edu.my/29582/
institution	Universiti Teknologi Petronas
collection	UTP Institutional Repository
description	Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90 and able to detect the collateral and non-collateral vessels from images.
format	Article
author	Abd Aziz, A. Izhar, L.I. Asirvadam, V.S. Tang, T.B. Ajam, A. Omar, Z. Muda, S.
spellingShingle	Abd Aziz, A. Izhar, L.I. Asirvadam, V.S. Tang, T.B. Ajam, A. Omar, Z. Muda, S. Detection of Collaterals from Cone-Beam CT Images in Stroke
author_sort	Abd Aziz, A.
title	Detection of Collaterals from Cone-Beam CT Images in Stroke
title_short	Detection of Collaterals from Cone-Beam CT Images in Stroke
title_full	Detection of Collaterals from Cone-Beam CT Images in Stroke
title_fullStr	Detection of Collaterals from Cone-Beam CT Images in Stroke
title_full_unstemmed	Detection of Collaterals from Cone-Beam CT Images in Stroke
title_sort	detection of collaterals from cone-beam ct images in stroke
publisher	NLM (Medline)
publishDate	2021
url	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122494766&doi=10.3390%2fs21238099&partnerID=40&md5=84a64ebf408bb1596c8f3d879eee1ca6 http://eprints.utp.edu.my/29582/
_version_	1741197265991630848
score	11.62408

Detection of Collaterals from Cone-Beam CT Images in Stroke

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