18 March 2016 Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease
Author Affiliations +
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Atefeh Abdolmanafi, Atefeh Abdolmanafi, Arpan Suravi Prasad, Arpan Suravi Prasad, Luc Duong, Luc Duong, Nagib Dahdah, Nagib Dahdah, } "Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97862U (18 March 2016); doi: 10.1117/12.2216943; https://doi.org/10.1117/12.2216943
PROCEEDINGS
7 PAGES


SHARE
Back to Top