Identification of ultrasonic echolucent carotid plaques using discrete Frechet distance between bimodal gamma distributions

Xiaowei Huang, Yanling Zhang, Long Meng, Ming Qian, Kelvin Kian Loong Wong, Derek Abbott, Rongqin Zheng, Hairong Zheng, Lili Niu

Research output: Contribution to journalArticlepeer-review

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 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.
Original languageEnglish
Pages (from-to)949-955
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume65
Issue number5
DOIs
Publication statusPublished - 2018

Keywords

  • atherosclerotic plaque
  • cardiovascular system
  • diseases
  • ultrasonic imaging

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