Trained cardiologists and image analysts can identify tissue types (calcified, lipid, and fibrous tissues) with some accuracy and repeatability, as shown in preclinical and clinical studies.4,6,12–14 Many use rules established by Yabushita et al.13 to manually classify tissues. However, modern OCT systems can create over 500 image frames in a single 2.5-s pullback scan, making manual image analysis for research very labor intensive, typically precluding measurements from every image frame. During a demanding clinical procedure, it would be even more difficult to manually analyze hundreds of image frames in clinical decision making. Although IVOCT image quality is outstanding, its limited depth penetration can sometimes confound plaque characterization,6,25–27 posing challenges for manual analysis, especially in the case of lipid or calcified plaques underlying a fibrous cap. Image quality is affected by any residual blood, but this is not a major concern with current IVOCT blood clearing strategies. Additionally, catheter eccentricity during IVOCT image acquisition may alter the appearance of various plaque features and confuse IVOCT image analysts.28 To be successful, an automatic computer classification algorithm will need to use all image information available to the human such as intensity, intensity changes, texture, border sharpness, three-dimensional (3-D) shape characteristics, and, perhaps most importantly, physical optical properties.