The ability to detect atherosclerotic plaque from optical coherence tomography (OCT) images by visual inspection is usually limited. We developed a texture based segmentation method using supervised and unsupervised classification to detect atherosclerotic plaque from OCT images without any reliance on visual inspection. Our Supervised method involves extraction of statistical textural features using the Spatial Gray Level Dependence Matrix (SGLDM) method, feature extraction and feature selection method, and application on supervised algorithm (K-nn). Our second method is based on unsupervised classification involves extraction of statistical textural features using the SGLDM method, application of an unsupervised clustering algorithm (K-means) on these features, and mapping of the segmented regions of features back to the actual image. We verified our results by visually comparing them to photographs of the vascular tissue with atherosclerotic plaque that we used to generate our OCT images. Our method could be potentially used in clinical cardiovascular OCT imaging.