TY - JOUR
T1 - Identification of ultrasonic echolucent carotid plaques using discrete Frechet distance between bimodal gamma distributions
AU - Huang, Xiaowei
AU - Zhang, Yanling
AU - Meng, Long
AU - Qian, Ming
AU - Wong, Kelvin Kian Loong
AU - Abbott, Derek
AU - Zheng, Rongqin
AU - Zheng, Hairong
AU - Niu, Lili
PY - 2018
Y1 - 2018
N2 - Objective: Echolucent carotid plaques are associated with acute cardiovascular and cerebrovascular events (ACCEs) in atherosclerotic patients. The aim of this study was to develop a computer-aided method for identifying echolucent plaques. Methods: A total of 315 ultrasound images of carotid plaques (105 echo-rich, 105 intermediate, and 105 echolucent) collected from 153 patients were included in this study. A bimodal gamma distribution was proposed to model the pixel statistics in the gray scale images of plaques. The discrete Fréchet distance features (DFDFs) of each plaque were extracted based on the statistical model. The most discriminative features (MDFs) were obtained from DFDFs by the linear discriminant analysis, and a k-nearest-neighbor classifier was implemented for classification of different types of plaques. Results: The classification accuracy of the three types of plaques using MDFs can reach 77.46%. When a receiver operating characteristics curve was produced to identify echolucent plaques, the area under the curve was 0.831. Conclusion: Our results indicate potential feasibility of the method for identifying echolucent plaques based on DFDFs. Significance: Our method may potentially improve the ability of noninvasive ultrasonic examination in risk prediction of ACCEs for patients with plaques.
AB - Objective: Echolucent carotid plaques are associated with acute cardiovascular and cerebrovascular events (ACCEs) in atherosclerotic patients. The aim of this study was to develop a computer-aided method for identifying echolucent plaques. Methods: A total of 315 ultrasound images of carotid plaques (105 echo-rich, 105 intermediate, and 105 echolucent) collected from 153 patients were included in this study. A bimodal gamma distribution was proposed to model the pixel statistics in the gray scale images of plaques. The discrete Fréchet distance features (DFDFs) of each plaque were extracted based on the statistical model. The most discriminative features (MDFs) were obtained from DFDFs by the linear discriminant analysis, and a k-nearest-neighbor classifier was implemented for classification of different types of plaques. Results: The classification accuracy of the three types of plaques using MDFs can reach 77.46%. When a receiver operating characteristics curve was produced to identify echolucent plaques, the area under the curve was 0.831. Conclusion: Our results indicate potential feasibility of the method for identifying echolucent plaques based on DFDFs. Significance: Our method may potentially improve the ability of noninvasive ultrasonic examination in risk prediction of ACCEs for patients with plaques.
KW - atherosclerotic plaque
KW - cardiovascular system
KW - diseases
KW - ultrasonic imaging
UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:47256
U2 - 10.1109/TBME.2017.2676129
DO - 10.1109/TBME.2017.2676129
M3 - Article
SN - 0018-9294
VL - 65
SP - 949
EP - 955
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 5
ER -