Atherosclerotic Risk Stratification Strategy for Carotid Arteries Using Texture-Based Features

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• 作者：U. Rajendra Acharya^&lowast ; ; S. Vinitha Sree^&dagger ; ; ^{vinitha.sree@gmail.com} ; M. Muthu Rama Krishnan^&lowast ; ; Filippo Molinari^&Dagger ; ; Luca Saba^&Dagger ; &Dagger ; ; Sin Yee Stella Ho^¶ ; ; Anil T. Ahuja^¶ ; ; Suzanne C. Ho^&lowast ; &lowast ; ; Andrew Nicolaides^&lowast ; &lowast ; ; ^# ; Jasjit S. Suri^&dagger ; ; ^&dagger ; &dagger ; ; ^{jsuri@comcast.net}
• 关键词：Atherosclerosis ; Symptomatic ; Asymptomatic ; Carotid ; Plaque ; Far wall ; Local binary patterns ; Laws&rsquo ; texture energy ; Wall variability ; Classifier ; Support vector machine ; Risk index
• 刊名：Ultrasound in Medicine and Biology
• 出版年：2012
• 期刊代码：82_03015629
• 类别：ph
• 出版时间：June, 2012
• 卷：38
• 期：6
• 页码：899-915
• 文件大小：1043 K

摘要

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鈥檚 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.