TY - GEN
T1 - Automatic atherosclerotic heart disease detection in intracoronary optical coherence tomography images
AU - Xu, Mengdi
AU - Cheng, Jun
AU - Wong, Damon Wing Kee
AU - Taruya, Akira
AU - Tanaka, Atsushi
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/2
Y1 - 2014/11/2
N2 - Intracoronary optical coherence tomography (OCT) is a new invasive imaging system which produces highresolution images of coronary arteries. Preliminary data suggests that the atherosclerotic disease can be detected from the intracoronary OCT images. However, manual assessment of the intracoronary OCT images is time-consuming and subjective. In this work, we present an automatic atherosclerotic disease detection system on intracoronary OCT images. In the system, a preprocessing scheme is first applied to remove speckle noise and artifacts caused by catheter. Intensity, Histograms of Oriented Gradients (HOG), and Local Binary Patterns (LBP) are then extracted to represent the OCT image. Finally a linear SVM classifier is employed to detect the unhealthy subject. Four-fold cross-validation process is conducted to evaluate the proposed system; and a dataset with 200 images from healthy subjects and 200 images from unhealthy subjects is built to evaluate the system. The mean accuracy is 0.90 and standard deviation is 0.0427, which indicates that the proposed system is accurate and stable.
AB - Intracoronary optical coherence tomography (OCT) is a new invasive imaging system which produces highresolution images of coronary arteries. Preliminary data suggests that the atherosclerotic disease can be detected from the intracoronary OCT images. However, manual assessment of the intracoronary OCT images is time-consuming and subjective. In this work, we present an automatic atherosclerotic disease detection system on intracoronary OCT images. In the system, a preprocessing scheme is first applied to remove speckle noise and artifacts caused by catheter. Intensity, Histograms of Oriented Gradients (HOG), and Local Binary Patterns (LBP) are then extracted to represent the OCT image. Finally a linear SVM classifier is employed to detect the unhealthy subject. Four-fold cross-validation process is conducted to evaluate the proposed system; and a dataset with 200 images from healthy subjects and 200 images from unhealthy subjects is built to evaluate the system. The mean accuracy is 0.90 and standard deviation is 0.0427, which indicates that the proposed system is accurate and stable.
UR - http://www.scopus.com/inward/record.url?scp=84929457782&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6943557
DO - 10.1109/EMBC.2014.6943557
M3 - Conference contribution
C2 - 25569925
AN - SCOPUS:84929457782
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 174
EP - 177
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Y2 - 26 August 2014 through 30 August 2014
ER -
