Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Using realistic coronary artery disease phantoms, we determined insignificant differences in mean and standard deviation estimates between our pullback analyses and more conventional processing of stationary acquisitions with frame averaging. There was no effect of tissue depth or oblique imaging on pullback parameter estimates. The method’s performance was assessed in comparison with observer-defined standards using clinical pullback data. Values (calcium , lipid , and fibrous ) were consistent with previous measurements obtained by other means. Using optical parameters (, , ), we achieved feature space separation of plaque types and classification accuracy of . Despite the rapid motion and varying incidence angle in pullbacks, the proposed computational pipeline appears to work as well as a more standard “stationary” approach.