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Appl. Sci. 2018, 8(9), 1632;

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM) & Media and Games Centre of Excellence (MagicX), 81310 UTM Johor Bahru, Johor, Malaysia
Faculty of Computing, Azad University, Marvdasht Branch 15914, Iran
Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Wilayah Persekutuan Kuala Lumpur, Malaysia
Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 50003 Hradec Kralove, Czech Republic
Technical University of Munich (TUM), 80333 Munich, Germany
School of Biosciences & Medical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM) & UTM-IRDA Centre of Excellence, 81310 UTM Johor Bahru, Johor, Malaysia
Department of Cybernetics and Biomedical Engineering, VŠB—Technical University of Ostrava, 70800 Ostrava, Czech Republic
Department Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
Department of Electrical & Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Faculty of Software and Information Science, Iwate Prefectural University, Iwate 020-0693, Japan
Author to whom correspondence should be addressed.
Received: 29 July 2018 / Revised: 2 September 2018 / Accepted: 5 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
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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. View Full-Text
Keywords: thin cap fibroatheroma; VH-IVUS image segmentation; texture feature; Particle Swarm Optimisation (PSO); back propagation neural network; Support Vector Machine (SVM) thin cap fibroatheroma; VH-IVUS image segmentation; texture feature; Particle Swarm Optimisation (PSO); back propagation neural network; Support Vector Machine (SVM)

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Rezaei, Z.; Selamat, A.; Taki, A.; Mohd Rahim, M.S.; Abdul Kadir, M.R.; Penhaker, M.; Krejcar, O.; Kuca, K.; Herrera-Viedma, E.; Fujita, H. Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features. Appl. Sci. 2018, 8, 1632.

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