  Coronary artery disease accounts for a large number of deaths across the
world and clinicians generally prefer using x-ray computed tomography or
magnetic resonance imaging for localizing vascular pathologies. Interventional
imaging modalities like intravascular ultrasound (IVUS) are used to adjunct
diagnosis of atherosclerotic plaques in vessels, and help assess morphological
state of the vessel and plaque, which play a significant role for treatment
planning. Since speckle intensity in IVUS images are inherently stochastic in
nature and challenge clinicians with accurate visibility of the vessel wall
boundaries, it requires automation. In this paper we present a method for
segmenting the lumen and external elastic laminae of the artery wall in IVUS
images using random walks over a multiscale pyramid of Gaussian decomposed
frames. The seeds for the random walker are initialized by supervised learning
of ultrasonic backscattering and attenuation statistical mechanics from
labelled training samples. We have experimentally evaluated the performance
using $77$ IVUS images acquired at $40$ MHz that are available in the IVUS
segmentation challenge
dataset\footnote{http://www.cvc.uab.es/IVUSchallenge2011/dataset.html} to
obtain a Jaccard score of $0.89 \pm 0.14$ for lumen and $0.85 \pm 0.12$ for
external elastic laminae segmentation over a $10$-fold cross-validation study.
