TY - GEN
T1 - Automatic image classification in intravascular optical coherence tomography images
AU - Xu, Mengdi
AU - Cheng, Jun
AU - Wong, Damon Wing Kee
AU - Taruya, Akira
AU - Tanaka, Atsushi
AU - Liu, Jiang
AU - Foin, Nicolas
AU - Wong, Philip
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/8
Y1 - 2017/2/8
N2 - Vulnerable plaque detection to identify plaque is important in coronary heart disease diagnosis. Currently, it is conducted through manual reading of intravascular optical coherence tomography (IVOCT) images by an interventional cardiologist. However, human reading and understanding is highly subjective. An objective and automated assessment of plaque status is highly needed. This paper proposes a method for automatic image classification in IVOCT images based on different lesion types. In the proposed method, we first use detail-preserving anisotropic diffusion to remove speckle noise in IVOCT images. It removes the noise without losing details. Then, the IVOCT images are transformed to polar coordinates for feature extraction. In particular, Fisher vector and other texture features including local binary pattern and histogram of oriented gradients are studied. Finally, a support vector machine classifier is obtained to classify the IVOCT images into five groups: Normal (normal), FP (fibrous plaque), FA (fibroatheroma), PR (plaque rupture), and FC (fibrocalcific plaque). These five groups are obtained according to lesion characteristics. We evaluate the proposed method in a dataset of 1,000 images with five groups. Experimental results show that the proposed method achieves an average accuracy of 90% in image classification. The proposed automatic IVOCT image classification method can be used to save time and cost of cardiologist.
AB - Vulnerable plaque detection to identify plaque is important in coronary heart disease diagnosis. Currently, it is conducted through manual reading of intravascular optical coherence tomography (IVOCT) images by an interventional cardiologist. However, human reading and understanding is highly subjective. An objective and automated assessment of plaque status is highly needed. This paper proposes a method for automatic image classification in IVOCT images based on different lesion types. In the proposed method, we first use detail-preserving anisotropic diffusion to remove speckle noise in IVOCT images. It removes the noise without losing details. Then, the IVOCT images are transformed to polar coordinates for feature extraction. In particular, Fisher vector and other texture features including local binary pattern and histogram of oriented gradients are studied. Finally, a support vector machine classifier is obtained to classify the IVOCT images into five groups: Normal (normal), FP (fibrous plaque), FA (fibroatheroma), PR (plaque rupture), and FC (fibrocalcific plaque). These five groups are obtained according to lesion characteristics. We evaluate the proposed method in a dataset of 1,000 images with five groups. Experimental results show that the proposed method achieves an average accuracy of 90% in image classification. The proposed automatic IVOCT image classification method can be used to save time and cost of cardiologist.
UR - http://www.scopus.com/inward/record.url?scp=85015424522&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2016.7848275
DO - 10.1109/TENCON.2016.7848275
M3 - Conference contribution
AN - SCOPUS:85015424522
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 1544
EP - 1547
BT - Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Region 10 Conference, TENCON 2016
Y2 - 22 November 2016 through 25 November 2016
ER -
