Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013652" target="_blank" >RIV/62690094:18450/17:50013652 - isvavai.cz</a>

Result on the web

<a href="http://www.sciencedirect.com/science/article/pii/S1568494616306858?via%3Dihub" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1568494616306858?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2016.12.048" target="_blank" >10.1016/j.asoc.2016.12.048</a>

Result language

angličtina

Original language name

Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images

Original language description

Thin cap fibroatheroma (TCFA) or &quot;vulnerable plaque&quot; is responsible for the majority of coronary artery death. Virtual Histology Intravascular Ultrasound (VH-IVUS) image is a clinically available method for visualizing color coded tissue maps. However, this technique has considerable limitations in providing medical relevant information for identifying vulnerable plaque. The aim of this paper is to improve the identification of TCFA in VH-IVUS image. Therefore, this paper proposes a set of algorithms for segmentation, feature extraction, and plaque type classification to accurately identify TCFA. A hybrid model using the FCM and kNN (HFCM-kNN) is proposed to accurately segment the VH-IVUS image. The proposed technique is capable of eliminating outliers and detecting clusters with different densities in VH-IVUS image. The next process is extracting plaque features to provide an accurate definition of the unstable (vulnerable) plaque. To achieve the above contribution, five algorithms are proposed to extract significant features from VH-IVUS images. Machine learning approaches are applied for training 440 in-vivo images obtained from 8 patients. Results proved the dominance of the proposed method for TCFA detection with accuracy rate of 98.02% compared with the 76.5% obtained by the cardiologist decision. Moreover, by validation of VH-IVUS images and their corresponding Optical Coherence Tomography (OCT) images, accuracy of 92.85% is achieved.

Czech name

—

Czech description

—

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

2017

Confidentiality

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

Name of the periodical

Applied soft computing

ISSN

1568-4946

e-ISSN

—

Volume of the periodical

53

Issue of the periodical within the volume

duben

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

380-395

UT code for WoS article

000395898900027

EID of the result in the Scopus database

2-s2.0-85010304666