ENGLISH
La vitrine de diffusion des publications et contributions des chercheurs de l'ÉTS
RECHERCHER

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

Téléchargements

Téléchargements par mois depuis la dernière année

Plus de statistiques...

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.

[thumbnail of Duong L 2016 13802 Classification of coronary artery tissues.pdf]
Prévisualisation
PDF
Duong L 2016 13802 Classification of coronary artery tissues.pdf - Version publiée
Licence d'utilisation : Tous les droits réservés aux détenteurs du droit d'auteur.

Télécharger (570kB) | Prévisualisation

Résumé

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.

Type de document: Compte rendu de conférence
Professeur:
Professeur
Duong, Luc
Affiliation: Génie logiciel et des technologies de l'information
Date de dépôt: 05 oct. 2016 18:14
Dernière modification: 29 nov. 2018 13:54
URI: https://espace2.etsmtl.ca/id/eprint/13802

Actions (Authentification requise)

Dernière vérification avant le dépôt Dernière vérification avant le dépôt