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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014667" target="_blank" >RIV/62690094:18450/18:50014667 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/18:10241573

Result on the web

<a href="https://www.mdpi.com/2076-3417/8/9/1632" target="_blank" >https://www.mdpi.com/2076-3417/8/9/1632</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app8091632" target="_blank" >10.3390/app8091632</a>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

APPLIED SCIENCES-BASEL

ISSN

2076-3417

e-ISSN

—

Volume of the periodical

8

Issue of the periodical within the volume

9

Country of publishing house

CH - SWITZERLAND

Number of pages

36

Pages from-to

1-36

UT code for WoS article

000445760200221

EID of the result in the Scopus database

2-s2.0-85053422578