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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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 |
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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 |
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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 |
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MDPI AG |
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2018 |
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http://eprints.utm.my/id/eprint/84320/ https://doi.org/10.3390/app8091632 |
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13.18916 |