Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Abstract

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

“…To compute this relationship, right skewed F-distribution (Fisher-Snedecor distribution) is used, which calculates the ratio of model mean square and residual mean square. The F-value is a measure of individual group deviation [83]. One-way ANOVA tests the null hypothesis that assumes the population means of 2 or more test samples are equal.…”

Carotid Plaque Classification Using Contourlet Features and Support Vector Machines

“…SVM classifier with polynomial kernel order 1 was used to classify the plaques and classification accuracy of 90.66% was obtained. Acharya et al (2012a) also proposed a CAD using gray scale features and classified the plaques as symptomatic or asymptomatic and classification accuracy of 82.4% was obtained. Kyriacou et al (2009) Acharya et al (2013b) using fuzzy classifier.…”

“…Classification has been performed using Adaboost and Support Vector Machines classifiers. To discriminate symptomatic and asymptomatic plaques and to help the physicians for quantifying the severity of plaques, an symptomatic asymptomatic carotid index has been used (Acharya et al, 2012a). Carotid atherosclerosis has been analyzed and assessed using three multiscale transforms (Tsiaparas et al, 2012).…”

Formal and Model Driven Design of the Bright Light Therapy System Luxamet

“…For example, breast imaging for cancer detection relies on sophisticated image processing algorithms [60,61]. Similarly, Computer-Aided Diagnosis (CAD) systems for plaque [62][63][64][65], cardiac disease [66,67] and diabetes [68,69] relay also heavily on computerized processing. Hence, these CAD systems stand to benefit from formal and model driven biomedical systems design, because the design methodology helps us to realize systemic safety and reliability.…”