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Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography

Yang Jianli, Zhang Bo, Wang Hongrui, Lin Feng, Han Yechen, Liu Xiuling

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 3

719--727

Identification of coronary atherosclerotic plaques and vulnerable plaques is of great clinical significance in the diagnosis and treatment of coronary artery diseases, such as myocardial infarction and sudden death. Optical coherence tomography (OCT) is a catheter-based intracoronary imaging technique with high resolution (<20 μm) adopted to study the morphology of atherosclerotic plaques and identification of the composition of plaques. Nevertheless, manual characterization and quantification of plaques by clinicians is a labor-intensive and subjective procedure. This study aimed to propose a novel plaque characterization algorithm to automatically characterize and classify the atherosclerotic plaques (fibrous, calcific, and lipid-rich). First, nongeometric features such as Fisher vector, principal component analysis, histogram of the oriented gradient, and local binary pattern were investigated and adapted to two geometric features (basic feature and texture feature) to characterize the plaques. Second, for automated classification of the plaques, a hard example mining strategy was introduced to train support vector machine classifier and improve the effectiveness of training data. Third, to demonstrate the relationship between the selected features and the plaque classification accuracy, different feature compositions and comparisons were presented. The contribution of key features to the final classification was revealed. Datasets from 20 OCT pullbacks of 9 patients were used in the training and testing using the proposed algorithm. The overall classification accuracy reached 96.8%, and that of fibrous, calcific, and lipid-rich plaques was 94%, 97.2%, and 99.2%, respectively.