Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization

Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an average accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Thus, it is evident that the selected features and the classifier combination can efficiently categorize plaques into symptomatic and asymptomatic classes. Moreover, a novel symptomatic asymptomatic carotid index (SACI), which is an integrated index that is based on the significant features, has been proposed in this work. Each analyzed ultrasound image yields on SACI number. A high SACI value indicates that the image shows symptomatic and low value indicates asymptomatic plaques. We hope this SACI can support vascular surgeons during routine screening for asymptomatic plaques.

Atheromatic™: symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture

Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Encouraged by this result, we feel that these features can be used to identify the plaque tissue type. Therefore, we propose an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

A 3-D ultrasound-based framework to characterize the echo morphology of carotid plaques

Carotid atherosclerosis is the main cause of brain stroke, which is the most common life-threatening neurological disease. Nearly all methods aiming at assessing the risk of plaque rupture are based on its characterization from 2-D ultrasound images, which depends on plaque geometry, degree of stenosis, and echo morphology (intensity and texture). The computation of these indicators is, however, usually affected by inaccuracy and subjectivity associated with data acquisition and operator-dependent image selection. To circumvent these limitations, a novel and simple method based on 3-D freehand ultrasound is proposed that does not require any expensive equipment except the common scanner. This method comprises the 3-D reconstruction of carotids and plaques to provide clinically meaningful parameters not available in 2-D ultrasound imaging, namely diagnostic views not usually accessible via conventional techniques and local 3-D characterization of plaque echo morphology. The labeling procedure, based on graph cuts, allows us to identify, locate, and quantify potentially vulnerable foci within the plaque. Validation of the characterization method was made with synthetic data. Results of plaque characterization with real data are encouraging and consistent with the results from conventional methods and after inspection of surgically removed plaques.