Characterization of
carotid atherosclerosis and classification of plaque into symptomatic or
asymptomatic along with the risk
score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the
stenosis. This application describes a statistical (a)
Computer Aided Diagnostic (CAD) technique for symptomatic versus
asymptomatic plaque automated classification of carotid
ultrasound images and (b) presents a cardiovascular
stroke risk
score computation. We demonstrate this for longitudinal
Ultrasound, CT, MR modalities and extendable to 3D carotid
Ultrasound. The on-line
system consists of Atherosclerotic Wall Region
estimation using AtheroEdge™ for longitudinal
Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a
feature extraction processor which computes: (a) Higher Order Spectra; (b)
Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line from the
Database of similar Atherosclerotic Wall Region images. The off-line Classifier is trained from the significant features from (a) Higher Order Spectra; (b)
Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Symptomatic
ground truth information about the training patients is drawn from
cross modality imaging such as CT or MR or 3D
ultrasound in the form of 0 or 1.
Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™
system is also demonstrated for Radial Basis
Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus
asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the
test set. The
system also yields the cardiovascular
stroke risk
score value on the basis of the four set of wall features.