Abstract：The objective of this study was automatic identification of carotid vulnerable plaques using ultrasound elastography
based support vector machine (SVM). Ultrasound radiofrequency data of 80 carotid atherosclerotic plaques from 52 volunteers
were acquired in the longitudinal view, and were used to estimate the strain rate distribution with an elastography algorithm.
Then the strain rate features of the plaques were extracted. Meanwhile, the plaques were classified to be stable or vulnerable
using high-resolution magnetic resonance imaging. The area under the receiver operating characteristic curve was used to
analyze each strain rate feature, and the maximum, 99th percentile, mean, and standard deviation of absolute strain rates were
selected and combined. The vulnerable plaques were identified using SVM with radial basis function, achieving sensitivity,
specificity, and accuracy of 70.0%, 88.0%, and 81.3%, respectively, in the testing dataset. This study validates the feasibility of
ultrasound elastography based SVM in automatic identification of carotid vulnerable plaques.
徐游民，刘志，何琼，罗建文. 基于超声弹性成像的支持向量机对颈动脉易损斑块的自动识别[J]. 中国医疗设备, 2019, 34(5): 15-19.
XU Youmin, LIU Zhi, HE Qiong, LUO Jianwen. Ultrasound Elastography Based SVM for Automatic Identification of Carotid Vulnerable Plaques. China Medical Devices, 2019, 34(5): 15-19.
