Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Ultrasound Med Biol. 2012 Jun;38(6):899-915. doi: 10.1016/j.ultrasmedbio.2012.01.015. Epub 2012 Apr 21.
Plaques in the carotid artery result in stenosis, which is one of the main causes for stroke. Patients have to be carefully selected for stenosis treatments as they carry some risk. Since patients with symptomatic plaques have greater risk for strokes, an objective classification technique that classifies the plaques into symptomatic and asymptomatic classes is needed. We present a computer aided diagnostic (CAD) based ultrasound characterization methodology (a class of Atheromatic systems) that classifies the patient into symptomatic and asymptomatic classes using two kinds of datasets: (1) plaque regions in ultrasound carotids segmented semi-automatically and (2) far wall gray-scale intima-media thickness (IMT) regions along the common carotid artery segmented automatically. For both kinds of datasets, the protocol consists of estimating texture-based features in frameworks of local binary patterns (LBP) and Law's texture energy (LTE) and applying these features for obtaining the training parameters, which are then used for classification. Our database consists of 150 asymptomatic and 196 symptomatic plaque regions and 342 IMT wall regions. When using the Atheromatic-based system on semiautomatically determined plaque regions, support vector machine (SVM) classifier was adapted with highest accuracy of 83%. The accuracy registered was 89.5% on the far wall gray-scale IMT regions when using SVM, K-nearest neighbor (KNN) or radial basis probabilistic neural network (RBPNN) classifiers. LBP/LTE-based techniques on both kinds of carotid datasets are noninvasive, fast, objective and cost-effective for plaque characterization and, hence, will add more value to the existing carotid plaque diagnostics protocol. We have also proposed an index for each type of datasets: AtheromaticPi, for carotid plaque region, and AtheromaticWi, for IMT carotid wall region, based on the combination of the respective significant features. These indices show a separation between symptomatic and asymptomatic by 4.53 units and 4.42 units, respectively, thereby supporting the texture hypothesis classification.
颈动脉斑块导致狭窄，这是中风的主要原因之一。由于狭窄治疗有一定风险，因此需要对患者进行仔细筛选。由于有症状斑块的患者中风风险更高，因此需要一种客观的分类技术将斑块分为有症状和无症状两类。我们提出了一种基于计算机辅助诊断 (CAD) 的超声特征化方法（一类 Atheromatic 系统），该方法使用两种数据集将患者分为有症状和无症状两类：(1) 半自动分割的颈动脉斑块区域和 (2) 自动分割的颈总动脉远侧壁灰阶内-中膜厚度 (IMT) 区域。对于这两种数据集，方案都包括在局部二值模式 (LBP) 和 Law 的纹理能量 (LTE) 框架中估计基于纹理的特征，并应用这些特征获取训练参数，然后将这些参数用于分类。我们的数据库包含 150 个无症状斑块和 196 个有症状斑块区域以及 342 个 IMT 壁区域。当在半自动确定的斑块区域上使用 Atheromatic 系统时，支持向量机 (SVM) 分类器的准确率最高，达到 83%。当使用 SVM、K-最近邻 (KNN) 或径向基概率神经网络 (RBPNN) 分类器时，远侧壁灰阶 IMT 区域的准确率为 89.5%。基于 LBP/LTE 的技术在这两种颈动脉数据集上均为非侵入性、快速、客观且具有成本效益，可用于斑块特征化，从而为现有的颈动脉斑块诊断协议增加更多价值。我们还为这两种类型的数据集分别提出了一个指标：AtheromaticPi，用于颈动脉斑块区域，和 AtheromaticWi，用于 IMT 颈动脉壁区域，这两个指标基于各自的显著特征进行组合。这些指标在有症状和无症状之间的分离度分别为 4.53 个单位和 4.42 个单位，从而支持纹理假设分类。
