Detection Of Atherosclerotic Plaque From Optical Coherence Tomography Images Using Texture-Based Segmentation

Abstract

Detection of atherosclerotic plaque from optical coherence tomography (OCT) images by visual inspection is difficult. We developed a texture based segmentation method to identify atherosclerotic plaque automatically from OCT images without any reliance on visual inspection. Our method involves extraction of texture statistical features (spatial gray level dependence matrix method), application of an unsupervised clustering algorithm (K-means) on these features, and mapping of the clustered regions: background, plaque, vascular tissue and an OCT degraded signal region in feature-space, back to the actual image. We verified the validity of our results by visual comparison to photographs of the vascular tissue with atherosclerotic plaque that were used to generate our OCT images. Our method could be potentially used in clinical studies in OCT imaging of atherosclerotic plaque.