Acharya U R, Sree S Vinitha, Mookiah M R K, Saba L, Gao H, Mallarini G, Suri J S
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.
Proc Inst Mech Eng H. 2013 Jun;227(6):643-54. doi: 10.1177/0954411913480622. Epub 2013 Mar 22.
In 30% of stroke victims, the cause of stroke has been found to be the stenosis caused by plaques in the carotid artery. Early detection of plaque and subsequent classification of the same into symptomatic and asymptomatic can help the clinicians to choose only those patients who are at a higher risk of stroke for risky surgeries and stenosis treatments. Therefore, in this work, we have proposed a non-invasive computer-aided diagnostic technique to classify the detected plaque into the two classes. Computed tomography (CT) images of the carotid artery images were used to extract Local Binary Pattern (LBP) features and wavelet energy features. Significant features were then used to train and test several supervised learning algorithm based classifiers. The Support Vector Machine (SVM) classifier with various kernel configurations was evaluated using LBP and wavelet features. The SVM classifier presented the highest accuracy of 88%, sensitivity of 90.2%, and specificity of 86.5% for radial basis function (RBF) kernel function. The CT images of the carotid artery provide unique 3D images of the artery and plaque that could be used for calculating percentage of stenosis. Our proposed technique enables automatic classification of plaque into asymptomatic and symptomatic with high accuracy, and hence, it can be used for deciding the course of treatment. We have also proposed a single-valued integrated index (Atheromatic Index) using the significant features which can provide a more objective and faster prediction of the class.
在30%的中风患者中，已发现中风的病因是颈动脉斑块导致的狭窄。早期检测斑块并将其随后分为有症状和无症状两类，有助于临床医生仅选择那些中风风险较高的患者进行有风险的手术和狭窄治疗。因此，在这项工作中，我们提出了一种非侵入性计算机辅助诊断技术，将检测到的斑块分为两类。利用颈动脉图像的计算机断层扫描（CT）图像提取局部二值模式（LBP）特征和小波能量特征。然后使用显著特征训练和测试几种基于监督学习算法的分类器。使用LBP和小波特征评估了具有各种核配置的支持向量机（SVM）分类器。对于径向基函数（RBF）核函数，SVM分类器的最高准确率为88%，灵敏度为90.2%，特异性为86.5%。颈动脉的CT图像提供了动脉和斑块独特的三维图像，可用于计算狭窄百分比。我们提出的技术能够将斑块自动高精度地分为无症状和有症状两类，因此，可用于确定治疗方案。我们还利用显著特征提出了一个单值综合指标（动脉粥样硬化指数），它可以提供更客观、更快的类别预测。
