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https://hdl.handle.net/2440/114290
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dc.contributor.author | Huang, X. | - |
dc.contributor.author | Zhang, Y. | - |
dc.contributor.author | Meng, L. | - |
dc.contributor.author | Qian, M. | - |
dc.contributor.author | Wong, K. | - |
dc.contributor.author | Abbott, D. | - |
dc.contributor.author | Zheng, R. | - |
dc.contributor.author | Zheng, H. | - |
dc.contributor.author | Niu, L. | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Biomedical Engineering, 2018; 65(5):949-955 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.uri | http://hdl.handle.net/2440/114290 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Xiaowei Huang, Yanling Zhang, Long Meng, Ming Qian, Kelvin Kian Loong Wong, Derek Abbott, Rongqin Zheng, Hairong Zheng, Lili Niu | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.rights | © 2018, IEEE | - |
dc.source.uri | https://ieeexplore.ieee.org/document/7867866/keywords?part=1 | - |
dc.subject | Bimodal gamma distribution; carotid plaque; discrete Frechet distance; ultrasound imaging | - |
dc.title | Identification of ultrasonic echolucent carotid plaques using discrete Fréchet distance between bimodal gamma distributions | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TBME.2017.2676129 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Abbott, D. [0000-0002-0945-2674] | - |
Appears in Collections: | Aurora harvest 8 Electrical and Electronic Engineering publications |
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