Identification of Ultrasonic Echolucent Carotid Plaques Using Discrete Fréchet Distance Between Bimodal Gamma Distributions.

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 linear discriminant analysis, and a k-nearestneighbor 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 (ROC) 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.