Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/114290
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Type: Journal article
Title: Identification of ultrasonic echolucent carotid plaques using discrete Fréchet distance between bimodal gamma distributions
Author: Huang, X.
Zhang, Y.
Meng, L.
Qian, M.
Wong, K.
Abbott, D.
Zheng, R.
Zheng, H.
Niu, L.
Citation: IEEE Transactions on Biomedical Engineering, 2018; 65(5):949-955
Publisher: IEEE
Issue Date: 2018
ISSN: 0018-9294
0018-9294
Statement of
Responsibility: 
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
RMID: 0030086710
DOI: 10.1109/TBME.2017.2676129
Published version: https://ieeexplore.ieee.org/document/7867866/keywords?part=1
Appears in Collections:Electrical and Electronic Engineering publications

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