FRANÇAIS
A showcase of ÉTS researchers’ publications and other contributions
SEARCH

Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease

Downloads

Downloads per month over past year

Abdolmanafi, Atefeh, Prasad, Arpan Suravi, Duong, Luc et Dahdah, Nagib. 2016. « Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease ». In Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling (San Diego, CA, USA, Feb. 27, 2016) Coll. « Proceedings of SPIE », vol. 9786. SPIE.
Compte des citations dans Scopus : 1.

[img]
Preview
PDF
Duong L 2016 13802 Classification of coronary artery tissues.pdf - Published Version
Use licence: All rights reserved to copyright holder.

Download (570kB) | Preview

Abstract

Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second- and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.

Item Type: Conference proceeding
Professor:
Professor
Duong, Luc
Affiliation: Génie logiciel et des technologies de l'information
Date Deposited: 05 Oct 2016 18:14
Last Modified: 29 Nov 2018 13:54
URI: http://espace2.etsmtl.ca/id/eprint/13802

Actions (login required)

View Item View Item