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|Title:||Identification of ultrasonic echolucent carotid plaques using discrete Fréchet distance between bimodal gamma distributions|
|Citation:||IEEE Transactions on Biomedical Engineering, 2018; 65(5):949-955|
|Xiaowei Huang, Yanling Zhang, Long Meng, Ming Qian, Kelvin Kian Loong Wong, Derek Abbott, Rongqin Zheng, Hairong Zheng, Lili Niu|
|Abstract:||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 Fr ´echet 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.|
|Keywords:||Bimodal gamma distribution; carotid plaque; discrete Frechet distance; ultrasound imaging|
|Rights:||© 2018, IEEE|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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