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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52573

Title: Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features
Authors: Zahra Rezaei
Ali Selamat
Arash Taki
Mohd Shafry Mohd Rahim
Mohammed Rafiq Abdul Kadir
Marek Penhaker
Ondrej Krejcar
Kamil Kuca
Enrique Herrera-Viedma
Hamido Fujita
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: thin cap fibroatheroma
VH-IVUS image segmentation
texture feature
Particle SwarmOptimisation (PSO)
back propagation neural network
Support Vector Machine (SVM)
Date: 2018-09-12
Issue Date: 2019-11-19T01:03:10Z
Publisher: Applied Sciences
Abstract: Abstract: 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.
Relation: 8(9)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52573
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