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Thin cap fibroatheroma detection in virtual histology images using geometric and texture features

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinicall...

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Main Authors: Rezaei, Zahra, Selamat, Ali, Taki, Arash, Mohd. Rahim, Mohd. Shafry
Format: Article
Published: MDPI AG 2018
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/84320/
https://doi.org/10.3390/app8091632
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spelling my.utm.843202019-12-28T01:46:43Z http://eprints.utm.my/id/eprint/84320/ Thin cap fibroatheroma detection in virtual histology images using geometric and texture features Rezaei, Zahra Selamat, Ali Taki, Arash Mohd. Rahim, Mohd. Shafry T Technology (General) Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque. MDPI AG 2018 Article PeerReviewed Rezaei, Zahra and Selamat, Ali and Taki, Arash and Mohd. Rahim, Mohd. Shafry (2018) Thin cap fibroatheroma detection in virtual histology images using geometric and texture features. Applied Sciences (Switzerland), 8 (9). p. 1632. ISSN 2076-3417 https://doi.org/10.3390/app8091632
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 T Technology (General)
spellingShingle T Technology (General)
Rezaei, Zahra
Selamat, Ali
Taki, Arash
Mohd. Rahim, Mohd. Shafry
Thin cap fibroatheroma detection in virtual histology images using geometric and texture features
description Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.
format Article
author Rezaei, Zahra
Selamat, Ali
Taki, Arash
Mohd. Rahim, Mohd. Shafry
author_facet Rezaei, Zahra
Selamat, Ali
Taki, Arash
Mohd. Rahim, Mohd. Shafry
author_sort Rezaei, Zahra
title Thin cap fibroatheroma detection in virtual histology images using geometric and texture features
title_short Thin cap fibroatheroma detection in virtual histology images using geometric and texture features
title_full Thin cap fibroatheroma detection in virtual histology images using geometric and texture features
title_fullStr Thin cap fibroatheroma detection in virtual histology images using geometric and texture features
title_full_unstemmed Thin cap fibroatheroma detection in virtual histology images using geometric and texture features
title_sort thin cap fibroatheroma detection in virtual histology images using geometric and texture features
publisher MDPI AG
publishDate 2018
url http://eprints.utm.my/id/eprint/84320/
https://doi.org/10.3390/app8091632
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score 13.211869

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