An automatic multi-class coronary atherosclerosis plaque detection and classification framework
An automatic multi-class coronary atherosclerosis plaque detection and classification framework
复制标题
DOI：
10.1007/s11517-018-1880-6
复制
发表时间：
2018-08
影响因子：
3.2
通讯作者：
Fengjun Zhao;Bin Wu;Fei Chen;Xin Cao;Huangjian Yi;Yuqing Hou;Xiaowei He;Jimin Liang
中科院分区：
文献类型：
--
作者：
Fengjun Zhao;Bin Wu;Fei Chen;Xin Cao;Huangjian Yi;Yuqing Hou;Xiaowei He;Jimin Liang
Detection of different classes of atherosclerotic plaques is important for early intervention of coronary artery diseases. However, previous methods focused either on the detection of a specific class of coronary plaques or on the distinction between plaques and normal arteries, neglecting the classification of different classes of plaques. Therefore, we proposed an automatic multi-class coronary atherosclerosis plaque detection and classification framework. Firstly, we retrieved the transverse cross sections along centerlines from the computed tomography angiography. Secondly, we extracted the region of interests based on coarse segmentation. Thirdly, we extracted a random radius symmetry (RRS) feature vector, which incorporates multiple descriptions into a random strategy and greatly augments the training data. Finally, we fed the RRS feature vector into the multi-class coronary plaque classifier. In experiments, we compared our proposed framework with other methods on the cross sections of Rotterdam Coronary Datasets, including 729 non-calcified plaques, 511 calcified plaques, and 546 mixed plaques. Our RRS with support vector machine outperforms the intensity feature vector and the random forest classifier, with the average precision of 92.6±1.9% and average recall of 94.3±2.1%. The proposed framework provides a computer-aided diagnostic method for multi-class plaque detection and classification.
