Stroke is the third leading cause of death in the western world and the major cause of disability in adults. The
type and stenosis of extracranial carotid artery disease is often responsible for ischemic strokes, transient ischemic
attacks (TIAs) or amaurosis fugax (AF). The identification and grading of stenosis can be done using gray scale
ultrasound scans. The appearance of B-scan pictures containing various granular structures makes the use of
texture analysis techniques suitable for computer assisted tissue characterization purposes.
The objective of this study is to investigate the usefulness of variogram analysis in the assessment of ultrasound
plague morphology. The variogram estimates the variance of random fields, from arbitrary samples in
space. We explore stationary random field models based on the variogram, which can be applied in ultrasound
plaque imaging leading to a Computer Aided Diagnosis (CAD) system for the early detection of symptomatic
Non-parametric tests on the variogram coefficients show that the cofficients coming from symptomatic versus
asymptomatic plaques come from distinct distributions. Furthermore, we show significant improvement in class
separation, when a log point-transformation is applied to the images, prior to variogram estimation. Model fitting
using least squares is explored for anisotropic variograms along specific directions. Comparative classification
results, show that variogram coefficients can be used for the early detection of symptomatic cases, and also
exhibit the largest class distances between symptomatic and asymptomatic plaque images, as compared to over
60 other texture features, used in the literature.