Classification of atorvastatin effect based on shape and texture features in ultrasound images

Abstract

Carotid atherosclerosis is the major cause of ischemic stroke, a leading cause of mortality and disability. Many research studies have been carried out on how to quantitatively evaluate local arterial effects of potential carotid disease treatments. In this paper, the atorvastatin effect evaluation on atherosclerosis plaques are classified based on various shape and texture features extracted from ultrasound images. First, images of atherosclerotic lesions were extracted manually from ultrasound images by an expert physician. After analysis, 26 shape and 85 texture characteristics, and vessel wall volume (VWV) percent of change, were extracted and calculated from atherosclerotic lesions. Among these, to make the method convenient and exact enough, effective features and VWV percent of change, were selected for drug treatment effect evaluation by physician. Finally, a support vector machine (SVM) classifier was utilized to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for evaluation. The classification results showed overall accuracy 91.67%, sensitivity 95.56%, specificity 86.16%; positive predictive value 90.72%, negative predictive value 93.20%, Matthew’s correlation coefficient 82.81%, Youden’s index 81.72%. And the receiver operating characteristic (ROC) curve in our test also performed well. The experimental results also demonstrate that classification using the combined features has higher accuracy than that only using shape/texture feature or VWV percent of change. The proposed method can be used for the statins effect evaluation, especially when patients are treated with drugs, and further be developed as a beneficial tool for facilitating a physician’s diagnosis of the atherosclerosis.